GVT_V7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TUIlmenauAMS/GVT_Lecture_Colab/blob/main/GVT_V7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Lecture \"Fundamentals of Video Technology\" and \"Digital Signal processing for Media Technology\"**\n",
        "\n",
        "## Gerald Schuller\n",
        "\n",
        "## gerald.schuller@tu-ilmenau.de"
      ],
      "metadata": {
        "id": "nhWJGkLav8mg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Lecture Video part 1:**\n",
        "\n",
        "https://youtu.be/uS4ANazNcsk\n"
      ],
      "metadata": {
        "id": "CyOmVFUiN-xl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import YouTubeVideo\n",
        "YouTubeVideo('uS4ANazNcsk')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "QdBwidbEOJvP",
        "outputId": "86316447-1ef2-4156-cba4-704e7182a3ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7fe7c98e4f40>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"400\"\n",
              "            height=\"300\"\n",
              "            src=\"https://www.youtube.com/embed/uS4ANazNcsk\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "            \n",
              "        ></iframe>\n",
              "        "
            ],
            "image/jpeg": 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pZ2brw1mUwubvK0o4YWep/VhAi29FTwLqtHaVisb3jjpWGnBeYs85ezp6r6fItovzsd5cSVNN216f0MSi9No3wvguv9g742jG2p3V3gRWXBpaLPH36SVJN214mOz3Vbtxt/0Q73wjh226yjK5JBSW1GJJtvOgtl79Vhza3PF4gZroFIX1q2BamvVWFtdusCQSAiAAAAAAAACCQBABIEAABYAAaeXe1R/M/wBDbSNTLf5lHtf6G4jM+0VqaCCZ6OBBpQAEAhkkMDXy9fwKv5WeaPTZb/Jq/kl8DzJz9uvhiqGzkrwj2r4GvUM2TaF2oz5X0vYskGsX2koxW4WPO5Yv4eTv+mUeEj0iPP5bG1KP9NWtHxuWM+/jRJANOISCQFgCQAACgACABNgqASLAfSqjw8BTWP4XFJ6NOxeBEsWksbYl6KwbtbqOzDIYLO3sJvFuzMlX2WrN3wwMWbHBZslfDUrFgvzdrNQx06dD6g2075l/fr0BNRd/Wfw2lG42S9ZY6sALKKbvmWem93rZaMVmpZtsbtbHpKppRaTnjbHXp6yXTTdrvRe99v8A0P8A0S4q1rPDQsfrWFST268PD9Czxs7tXt5lFC7sptW92iyAq7e1mz+nf9CzWc72mnhbh+5Zu11nY4am7FFNWaU8bttv66holQtjeWzT7jOYFdtPOw0q2Ny6Teb63s6et2LRkBRxljZrqKyjU/C17+0mDKCkFLXYh5+Ns3XYC9gUjn67EpO7xw1YAWBjlKV8EuJGfPc8S4MoMXOS3eHaTGbelW+n9e8YMgAIFiCQECLEgK08pi3Upu2Cvdm2Y6mrtRlJIKyRFiuVV4UoOc3aK8epHk+UeXKlZuMLwp7FpfazPr1PP108fx338enqZdRi7SqQT2ZyMsJxkrxaa2p3R4OMTNQrTpu8JNPq0HP/AJXe/wDzfn5XuLFZHK5N5cjO0K1oy1S0RfkdW6eh3R1ll+PP683zcrDla/hVPyy+B5k9TlC/hz/LL4Hl9Rj234YqhkyZ+rw+JSaLZNofvM+V9NlrFk2JYsYv1ufBI4mXx9Sut2vfjBM7yRx+UI//ACl/6Z/p+hYnr44xIJRpwCQAoCQEACbARYmxICoSJACFiLEkhX0TOV3nPZYlZXTjhnxVtskZM1OxrT5MTk3nyxberadbWYyvK6b/ANSPukg60H/qLX+NGv6IW++CIlyTZN57wu/ZTH6uRuSrReiol2SRKqR31q/EjmPkyS/ErrS83qbfbq4mN8mtySusLu2b1pcLvgTVyOxnYJKavreDuWv/AFX4HG9HSw9aK0WbjhZ3tfhftaKz5OagqjcMVfNzbLaNMdx6LL4XKtSetd366jjPk6Sa/l2u1dprRpwKVMhkle8VbVd39lX+KLpjvNPqv2FUsdXA4T5Nq3wmsZZq9ZrFX8mSuT66jKXOeze6z5X0XG/6Md/ZfQSrHnlkmUaFOWlpfxJYtOxEaGU2uqkra3nystOngTo5/wBvRXGcedcMqSbzp4Y+3qwx8S04ZYnbOlq/Fe13ZeJdOXoLoXPOqeV6pSta+mOglV8rws5P3R22+I6OXocBc86sqyu1/W1fhjj9WZLyzKlpvov7C0fTQ05ehIucD0hlK/D/AIFfv2UPHm076+bY6TmvQg8/DlWtpVOL1XUZdu0hcqz/ANtdftL9R1DmvQg5uRV5VIxlaKvfDOd8GbizraFxZUxmBgals4S/YWlsfe/YqM4MWdLZ4kOUrWs77cALVNK7UTWqKEXKWhIrG7te+Gs5n2mruFKEV+KXgiVZNcHlnlGVedr2itCNGETFlM2tDx19Rqxqa1JM8vr9r3+M8zHUiWvFaXY1sjk53xMFWortN2MY666KzZaHc2ch5Wnk01GXrUpNdsew5GT1Vf1Xjsatc3cpjnQv7zfnY5+5PUe0lNTpuUXdOLtwPMo2fszlbcZ0ZO+F49mtGujr7/p5fMy1imTk+h+8TIybX2mYvr43kLClo9yL2J6+tefiEjl5fH16620E+7JnWSOflkb1oreo1o/BiJ6+POglA04hNgSgiCSQBCRJIAAE2AgEgCASAPpa0ls9I87HLamucuJb79Pfl4M301w9Bzi2kSq20Y4nBWXS35d2PkT9+lvvuQ8i9ROK73O9T26ApRTwWL0tROD9/nvY9cIW+BaHKM1F+vaV1a0Y2twJ1F5rtVpYP1XJJN9rtoLc6sMHbH8Lw1WscX0pUx9ddXqolcqT349xjqHNdmVVWeDeF7Wf1cOcderqvicb0rUxxh3Xj4kS5UqXXsP3SX6l6hzXaz4dWOOjSIyjm6FFbGkvA48uUWpNJwaVsfXx8SVyrNYfw+Mh1DmutOVO1nm42ww1sqpU1K1oppdSSjoOb6Wlu0+80T6We5Dv/sOonNdGUaaWKjZY+76sXlCMnfBtW19d0clcs7aS737ErliP+34ryHUOa6joQ2arLF4aNGzQisaML3V9ml2um+OLek53peD00nxQfKtPVBp6bpRdy9Q5rorJ4JRjjdWs7u+CsS8mhe9nfTpe1P4pHL9JU7ey+5HzMvpWk7XUrrXZeY2GVv8A3dY2usbrRg73vxJhBR9W+rBYX7Tnx5To4e3h2+ZPpGg23eSb0+0vgNiZW591VrZzvovhe1rW0B5PHNnG+ErrVdXwNX0jRf8AqS/zH3+i5Xz/AH2e1PZ1IbDK2JZIm21hd44dmjZoEMltLOvtwtw4XfExRy6n/u9f1gX++03/AKqXVdWGmVZ5L6zlnPFvDq0/HEvQpZqthq0dmkwrKKeP8eL0aZRwLU6sEklVg7LXKLv16S6mNmwsYFU2VIvC2rTt0mSM+tMC55j7VVE6tGOtJt+89Pc8d9o7rLGna2bGS99/Iz6v434n64dannSvqvxKxySKWhpdZt5yRrV6zbw0LxPNa93mTGfJYZrL18ji2pL2jQp5VO+Kt2Gzz1SaalBJamnihjUsZaOTJXwaztOGs2nH1WjBkeVY5s9JvTtqEPWRqcl1HSqqSwzcX/Uta4G+adG0pxs8YSd7rareRupK2Gg3uvP685f+2KZWhpfajJMx0tL9wjFb1DQZTFkz0mewv08/EJGnlitWyZ7ZTjxi/I3kjT5Sw5iW7Wp+N1+oi15hxs2tmBBmyuObWqrZOXxMRpwCQABIAAlAkCCQSBAJARAACvR2FiwI7K2FiwArYixcBVLCxYWApYWL2FgKWFi1hYCthYtYWArYWLWFgKixawsBUFrCwFUhYtYWAqC1hYCoLWFgiAWsLDTEE+4mwsNMEzlcpyfPLDTHTxOtY0OU6EnaUdmJK15yVzKsHgYpzUVjoNi94pmN0k8WrmK7T4wKTeKjpNqNeUIpzj6u1YkwijbopWJreNRU1NqUU0+GBv0ovNtr0FXC2O0yp5qvsVyz6zVadPm587fRbDrNs5XJk5Sd3jeV7bDrNGsxx9eumKZhjpZnma70+5ljFb2Sv1n2G0aeSP112G80KQRpcrr+BJ7sqcuE0byNblSN8mr/APrm+Cv+gi153lWNspq9bT4pM1Te5XxqxlvU4S8LfoaRpwoAAAJAAkIAAABIAAAAD0gJBHdAJAEAkAQCQQQCbAogEgCASAIBIAgE2FgIBNhYCATYWAgFrCwFQWsLEEWFi1gBFhYsLAVsJK6aLADz86Tg5Rs83VcQsdjlCkpUpPWlde44ErrQZsdPNbSsbFJo47ryL08plfWZ5dO47c2rGjyjXap5sb503mqxNKcptajX5Uyx0ZUebavnO+Cdzp5n6x7v5ro8k5JKnD1tL26TeYoVecpxmvxK9thLFcoxTRgenj8DZma7WKIM+Se3D3nSOZkntU+06jNVIgplEM6nOO2E1xTRkRZLQRXk8uedTyWe9RiuD/c0xQyrPUKNSSiqbnFSthGPXbHV4nS9Cz3l3Z+RquVm/HNB0vQ1Tejwl5Eeh6m9H/JfoEyueDoeh6m9Di/Ir6JqbYd8GVpA3fRVXbDvoj0XV/o/uR8wZWmDb9F1dke/HzHoyrurvx8wZWoLm16Nrbn+UfMeja25/lHzCZWqDZ9HVtx8V5j0fW/25eAMd0E2JMu6pJNhYCBYkAQCQBBFiwsBUFhYCASLBUCxIAgEgAAAAJAEAkAQSBYIAmxNgIsCSQKgtYWIOby9OUcmk4OzvHhfQcSjXUlfiZeW+VOdqxo03/Djdye9LyRzKcs2WnDYzfP4zPeXHTUUzcVOKijkrKFbBotHLrK0nfZYxzXbvz/l0ZVLXsczKrSmm9RSeVuWC0FYK7R08fx5drj/ACfyyzny6nJ/LUaf8KqvVjgpLVrxR3KdSNSOdCSlHanc8PNrnKnaSpyj7La7G0a9eZWPPr8e2mjBJYrtR5JcoV46Ks+N/ibFPlystObLtWPgY5a6enoYOH5kdZnjaX2htbOpa74T/Y7eT/abJp+05U3/AFLDihlWV1mRms16PKmT1HmwrU3LZnJM3FiZxrXzTKv4VevBpu05rB2wub6+0b/233zF9o6Vsuyjrknxin+pzMw6uOu0vtI9yXfLdJXuz75w80ZhMhtd5fab+mffLL7T9VTvHnswZgyG16LpQv8Ay8V5ll9qF/5PA83mDMGQ2vTdKI7anBE9JobZ91HmMwZow6r1C+0tPel3ESvtJT3n/bPLZhOaMOq9T0ipb3/1/sOkVLeX9v8AY8tmDNGHVfRQSDm7IAJAAACASAoAAIBIAAAAALABYkARYmwJArYkkARYWJJAiwsSAgCbCTSV20ltbsgCQscrlDl6nSTjT9efV7K9+v3HnMs5VrVsJTeburCPA1PLN9PVZVyvQpYOedLZD1n5HC5R5clWThBZkNeN5SWxnGcg2anmRi+rVVP+JcyTd8TCljcyuDeKNMVVU+svCldmJp7S0aMnr8QjYcox8kWoNttvBaikKCjiyZTvhDia0YH7crE5wzLSZNjLcVeJFiSbARYZpKRewGKasev+yOWuUJUJPGPrQ/LrX1tPI1dXab2QZTKjOFSOmLvbataGasuV6PlSmvvE87Q4wa0bLPV1GrzNP6zPI2+V2qkqVSLvGdNNP3t/qc/mzlW2X7vT+lDyI+50ti7kPIxOmMwDI8gpbsf7cCvo+lux/trzKZgzALejaW7H+3+49GUt2PcfzFbMWe18RpifRdPZHuy8yvoqlsj/AJ+ZOO18Sby2viNTFPRFLZHvTK+iKexd+fkZLy2viTnS2viNMYfRFPYv7kvIeh4bF/cfymbOltYz5bWNXHfBJBGgAkCCQAqCQAIBIsBBIJAgE2AEWJsCQIBIAgkAAASBAJAAEk2CMWUVVTpzm9EYuXBHhMq5Qq13epJvTZfhXYj1/wBoaqhklRa52iuu78kzw8TfmOfqrXBBJtkJIJAvRje5lUbYorQRd4BlZxjLSiOat7PxLJFbNPAoj7u2/Wd+oyZqQUyGwNeovWDRNbSiWiNRjJsC0Qqti6RDReKwAwVvw9plTwMdbTHtDkX4ju8l5Tn0lTemm5OP5XbDjfibmYcPk2vzdWN9Ddn2M9O6Jz9z9dPLTcCMw2+aI5ow01c0jMNvmiHSINRxGabXNDmgNXNIzTa5ojmwNbNGabPNEc2BrZozTZ5sc2B0wCTQgEkICQARQEoAQCQBAJAAAFAAAASAABNiALAkCLEgBAkBAeY+1mUXqUqSeEYuTXW9HgnxPOrSbnK1fPyqu/62l2LD9DT1nWfHKpBIZUQSCQM9HQjJJGOloMtgyrBmQx2xMkSiLEF2VZBr5RqJYyjUTqDUUaESWSkFLF4hCRYla9d+sit0itZ+sUQqMqldo93kVVVKUJ7Ur9us8LRjrPUfZivdVKT1WlH36f0M2bGvN/XYzCMwy2Isc3VjzCMwy2IsBizBmGSwsRWLMIzDNYiwGLmyObM1iLDEYubI5szCwxVgeP6V5RuUu7L5h0ryjcpd2XzGuax1HsAeP6V5RuUu7L5h0ryjcpd2XzDmnUexB47pXlG5S7svmHSvKNyl3ZfMOadx7EHjuleUblLuy+YdLMo3KXdl8w5p3HsQeO6V5RuUu7L5h0ryjcpd2XzDmncexB47pZlG5S7svmHSzKNyl3ZfMOadx7Ek8d0syjcpd2XzDpZlG5S7svmHNO49iDx3SzKNyl3ZfMOlmUblLuy+Yc07j2IPHdLMo3KPdl8w6WZRuUe7L5hzTuPZA8b0tyjco92XzDpblG5R7svmHNO49mDxvS3KNyj3ZfMOluUblHuy+Yc07j2RJ4zpdlG5R7svmHS7KNyj3ZfMOadx7MXtjqWJ4zpdlG5R7svmKVvtVlE4Sg40kpJxuoyurrV6w5qdRzKk86Upa5Nyfa8StzHzr6hzjOjDZDNdVn1E8+9iAzEo1+efUTz76gN6noMyObHK5LUi332WyPBhG/JEo57y6WyPBj79LZHgwOiQzQ+/T2R4PzI+/S2R4MDZrlo6EaUsqk9SCyuWxeIWNxkI1PvUti8R96lsXiF1vJlZM01lcti8Q8qk9S8SyslTGTLRMDqO9yedfUQbcGdbkCrm5TDZJOPh+xwI12thkpZdOElKNlJO6eOkqvpViLHjel+U7lHuy+YdL8o3KPdl8xzxvqPY2FjxvS7KNyj3ZfMOl2UblHuy+YYvUexsLHjul2UblHuy+YdLso3KPdl8w5Oo9hYWPH9Lso3KPdl8xHS3KNyj3ZfMOTqPYWFjx/S3KNyj3ZfMOlmUblHuy+YcnUevsLHkOlmUblHuy+YdLMo3KXdl8w5Oo4IANuQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/9k=\n"
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Lecture Video part 2:**\n",
        "\n",
        "https://youtu.be/_-jG1m11Ag0"
      ],
      "metadata": {
        "id": "7WGOOmClOO0P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "YouTubeVideo('_-jG1m11Ag0')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "HCqjVdDyOSD-",
        "outputId": "9b7768f8-99c3-406d-f3d0-661dfb8593ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7fe7c98f5af0>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"400\"\n",
              "            height=\"300\"\n",
              "            src=\"https://www.youtube.com/embed/_-jG1m11Ag0\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "            \n",
              "        ></iframe>\n",
              "        "
            ],
            "image/jpeg": 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          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Quantization, conversion of values into bits**\n",
        "We have seen that for pure brightness values we need about 100 steps, for which 7 bits are sufficient. What is the situation with DCT coefficients? We saw with the Contrast Sensitivity Function that the eye has different sensitivity for different spatial frequencies. Since our **DCT coefficients** now represent **different spatial frequencies**, this also results in different sensitivities.\n",
        "\n",
        "How do we establish the connection between the **DCT coefficients** or sub-bands and the **spatial frequencies** (in brightness oscillations or periods per degree)? To do this, we need to know how many pixels per degree appear to the eye at a given viewing distance and pixel size."
      ],
      "metadata": {
        "id": "MmRlEIWsv-1S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Example**: We have a 50cm wide screen with 1000 pixels over this width and a viewing distance of 50cm.\n",
        "\n",
        "1 pixel has the width of 50cm/1000=5/100 cm. It appears under an angle of arctan ((5cm/100)/50cm)=1e-3 Rad=0.057 Degrees.\n",
        "So we get 1/0.057=17.45 pixel/degrees.\n",
        "Since each pixel represents a sampled value, this now corresponds to the **sampling frequency**.\n",
        "The Nyquist frequency is therefore 17.45/2=8.725 periods/degree."
      ],
      "metadata": {
        "id": "bv2M_Pu7wPyL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 8x8 block based DCT divides the frequency range from **0 to Nyquist** into 8 equal **subbands**. Each has a width of 8,725/8=1,0906 periods/degree.\n",
        "\n",
        "In this way, we have now obtained an assignment of the sub-bands or coefficients (the index k) of the DCT to the spatial frequency. The Contrast Sensitivity Function then shows us the **relative sensitivity** of the eye to the **different** sub-bands.\n",
        "\n",
        "This in turn shows us how best to quantize the DCT coefficients. The **higher** the **contrast sensitivity** in a subband, **the more accurate** we have to quantize the corresponding DCT coefficient. The coefficients of the **higher spatial frequencies** tend to require a **lower accuracy** and thus **fewer bits**.\n",
        "\n",
        "How do we now **convert** our **DCT coefficients into bits** with appropriate accuracy?\n",
        "\n",
        "We can divide our possible range of values into equal **intervals**. We can then assign an **index** to each of these intervals, which we can display and transfer with a binary **code word**.\n",
        "\n",
        "Depending on whether the value \"0\" appears in the middle or at the edge of a quantization interval, we distinguish between the so-called \"**Mid-Tread**\" and \"**Mid-Rise**\" quantizers."
      ],
      "metadata": {
        "id": "A8VVuvX1wjrm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Example**: The **Mid-Tread** quantizer (see: https://en.wikipedia.org/wiki/Quantization_ (signal_processing).\n",
        "\n",
        "In the encoder and for the Mid-Tread case, the index I is calculated from a value, $x$, by means of a simple rounding, according to the formula\n",
        "\n",
        "$$I=round(\\frac{x}{Δ})$$\n",
        "\n",
        "where Δ is the **quantization stepsize**. The reconstructed value,  $x_{rek}$,  is calculated in the **decoder** by the so-called **de-quantizer**, using the formula\n",
        "\n",
        "$$x_{rek}=I⋅Δ$$"
      ],
      "metadata": {
        "id": "-ISDtdW9xRTH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we want to use b bits to represent the indices of this range of values, index 0 represents the smallest possible value \"min\", and the largest index $2^b-1$ represents the largest possible value \"max\" (if b is the number of bits we want to spend on the index), then\n",
        "we can divide the value range (max-min) into $2^b-1$ equal intervals. Each interval then has the step height Δ of\n",
        "\n",
        "$$Δ=\\frac{max-min}{2^b-1}$$\n",
        "\n",
        "**Note**: This formula also works for b=0 bits (no transfer) if we replace 1 with 0.99 to avoid division by 0!\n",
        "\n",
        "The difference between original and reconstruction is the “quantization error” $e$\n",
        "\n",
        "$$e=x_{rek}-x$$\n"
      ],
      "metadata": {
        "id": "auAxZdg7yCS0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Example**: We have our normalized range with min=0 and max=1, b=2 bits and x=0.3.\n",
        "Then our quantization step size is\n",
        "\n",
        "$$Δ=\\frac{max-min}{2^b-1}=\\frac{1}{3}=0.33...$$\n",
        "\n",
        "the quantization index is\n",
        "\n",
        "$$I=round(\\frac{x}{Δ})=round(\\frac{0.3}{0.33...}=round(0.9)=1$$\n",
        "\n",
        "the reconstructed value in the decoder is\n",
        "\n",
        "$$x_{rek}=I⋅Δ=1⋅0.33...-0.3=0.033...$$\n",
        "\n",
        "and the quantization error is\n",
        "\n",
        "$$e=x_{rek}-x=0.33...-0.3=0.033...$$"
      ],
      "metadata": {
        "id": "Jy3Uqpbgyobo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Example of the binary representation** of the index for transmission from the encoder to the decoder, here including negative numbers:\n",
        "\n",
        "2 bits or 4 indices:\n",
        "\n",
        "|Index|Binary representation(code word)|\n",
        "|----------|:------------|\n",
        "|0|00|\n",
        "|1|01|\n",
        "|-2|10\n",
        "|-1|11|\n",
        "\n",
        "The binary representation is also called \"**code word**\"."
      ],
      "metadata": {
        "id": "XyDKulx9zZLY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note** that the example shows the so-called **two's complement** for negative numbers, see also https://de.wikipedia.org/wiki/Zweierkomplement\n",
        "\n",
        "or\n",
        "\n",
        "https://en.wikipedia.org/wiki/Two%27s_complement\n",
        "\n",
        "The code word for negative numbers is obtained by taking the binary representation of the positive number, inverting its bits and adding 1."
      ],
      "metadata": {
        "id": "KFAwYY_O0bPS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**: The rounded up logarithm to base 2 of the number of indexes ($MaxIndex-MinIndex+1$) indicates the required **number of bits**:\n",
        "\n",
        "$$bitnumber=ceil(log_2(MaxIndex-MinIndex+1))$$\n",
        "\n",
        "In the above example:\n",
        "\n",
        "$$bitnumber=ceil(log_2(4))=ceil(\\frac{log(4)}{log(2)})=2$$"
      ],
      "metadata": {
        "id": "8AMq33SL0kUI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Implementing in Python**\n",
        "The conversion of indexes to and from code words works best in Python with the so called. “**Dictionaries**”.\n",
        "\n",
        "Example: Dictionary for converting an index (e. g. index=-1) into a code word:\n",
        "The “codebook” is defined as:"
      ],
      "metadata": {
        "id": "PwoIWaDI1JLw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "codeword={0:'00',1:'01',-2:'10',-1:'11'}"
      ],
      "metadata": {
        "id": "z_sZdPR_1RU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The conversion from index to code word (coding) is done by:"
      ],
      "metadata": {
        "id": "xsFr1kaM1UkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "codeword[-2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "kOIMueHT1ZnC",
        "outputId": "19813790-35e9-44ee-ebe5-f64f20550886"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'10'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This binary codeword is then transmitted to the decoder. The decoder needs to convert the codeword back into an index first. The conversion of a code word into an index in the decoder is done by defining the reverse Dictionary"
      ],
      "metadata": {
        "id": "lahL8Ivu1cMo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "decodeword={'00':0,'01':1,'10':-2,'11':-1}"
      ],
      "metadata": {
        "id": "CWrOzyd21eD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "and then decode with"
      ],
      "metadata": {
        "id": "4WbcOnBK1hkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "decodeword['11']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdLTR59i1iVY",
        "outputId": "0c798009-eb65-43a1-e751-aff3926b2ac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Python example for direct quantization of the pixels of the Y-component of the video frames:**\n",
        "We generate the quantization steps with:"
      ],
      "metadata": {
        "id": "hKRyPuoF1kif"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "bits=2\n",
        "#resulting quantization step size for 2^bits steps, with min=0 and max=1:\n",
        "#Dividing the value range by the number of\n",
        "#available quantization intervals,\n",
        "#to obtain the quantization step size:\n",
        "quantstufe=1.0/(2**bits-1)\n",
        "print(\"quantstufe=\", quantstufe)"
      ],
      "metadata": {
        "id": "bQwWcKGe1pc9",
        "outputId": "e996adb2-b959-42c0-e53d-0031260fefa0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quantstufe= 0.3333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The quantization indices are calculated from the frame Y with:"
      ],
      "metadata": {
        "id": "rOdSEwOa1sCE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Videostream (please run this first)\n",
        "from IPython.display import display, Javascript, Image\n",
        "from google.colab.output import eval_js\n",
        "import html\n",
        "from base64 import b64decode, b64encode\n",
        "import cv2\n",
        "import numpy as np\n",
        "import PIL\n",
        "import io\n",
        "\n",
        "def jsob_to_image(js_object):\n",
        "  # decode base64 image\n",
        "  image_bytes = b64decode(js_object.split(',')[1])\n",
        "  # convert bytes to numpy array\n",
        "  img_array = np.frombuffer(image_bytes, dtype=np.uint8)\n",
        "  # convert numpy array into OpenCV BGR\n",
        "  frame = cv2.imdecode(img_array, flags=1)\n",
        "\n",
        "  return frame\n",
        "\n",
        "#JavaScript code to get the stream\n",
        "def video_stream():\n",
        "  js = Javascript('''\n",
        "    var video;\n",
        "    var div = null;\n",
        "    var stream;\n",
        "    var captureCanvas;\n",
        "    var imgElement;\n",
        "    var labelElement;\n",
        "\n",
        "    var pendingResolve = null;\n",
        "    var shutdown = false;\n",
        "\n",
        "    function removeDom() {\n",
        "       stream.getVideoTracks()[0].stop();\n",
        "       video.remove();\n",
        "       div.remove();\n",
        "       video = null;\n",
        "       div = null;\n",
        "       stream = null;\n",
        "       imgElement = null;\n",
        "       captureCanvas = null;\n",
        "       labelElement = null;\n",
        "    }\n",
        "\n",
        "    function onAnimationFrame() {\n",
        "      if (!shutdown) {\n",
        "        window.requestAnimationFrame(onAnimationFrame);\n",
        "      }\n",
        "      if (pendingResolve) {\n",
        "        var result = \"\";\n",
        "        if (!shutdown) {\n",
        "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
        "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
        "        }\n",
        "        var lp = pendingResolve;\n",
        "        pendingResolve = null;\n",
        "        lp(result);\n",
        "      }\n",
        "    }\n",
        "\n",
        "    async function createDom() {\n",
        "      if (div !== null) {\n",
        "        return stream;\n",
        "      }\n",
        "      div = document.createElement('div');\n",
        "      div.style.border = '2px solid black';\n",
        "      div.style.padding = '3px';\n",
        "      div.style.width = '100%';\n",
        "      div.style.maxWidth = '600px';\n",
        "      document.body.appendChild(div);\n",
        "\n",
        "\n",
        "      video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      video.width = div.clientWidth - 6;\n",
        "      video.setAttribute('playsinline', '');\n",
        "      video.onclick = () => { shutdown = true; };\n",
        "      stream = await navigator.mediaDevices.getUserMedia(\n",
        "          {video: { facingMode: \"environment\"}});\n",
        "      div.appendChild(video);\n",
        "      imgElement = document.createElement('img');\n",
        "      imgElement.style.position = 'absolute';\n",
        "      imgElement.style.zIndex = 1;\n",
        "      imgElement.onclick = () => { shutdown = true; };\n",
        "      div.appendChild(imgElement);\n",
        "\n",
        "      const instruction = document.createElement('div');\n",
        "      instruction.innerHTML =\n",
        "          '<span style=\"blue: red; font-weight: bold;\">' +\n",
        "          'click here to stop the video</span>';\n",
        "      div.appendChild(instruction);\n",
        "      instruction.onclick = () => { shutdown = true; };\n",
        "\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "      captureCanvas = document.createElement('canvas');\n",
        "      captureCanvas.width = 640;\n",
        "      captureCanvas.height = 480;\n",
        "      window.requestAnimationFrame(onAnimationFrame);\n",
        "\n",
        "      return stream;\n",
        "    }\n",
        "    async function stream_frame() {\n",
        "      if (shutdown) {\n",
        "        removeDom();\n",
        "        shutdown = false;\n",
        "        return '';\n",
        "      }\n",
        "      var preCreate = Date.now();\n",
        "      stream = await createDom();\n",
        "\n",
        "      var preShow = Date.now();\n",
        "\n",
        "\n",
        "\n",
        "      var preCapture = Date.now();\n",
        "      var result = await new Promise(function(resolve, reject) {\n",
        "        pendingResolve = resolve;\n",
        "      });\n",
        "      shutdown = false;\n",
        "\n",
        "      return {'create': preShow - preCreate,\n",
        "              'show': preCapture - preShow,\n",
        "              'capture': Date.now() - preCapture,\n",
        "              'img': result};\n",
        "    }\n",
        "    ''')\n",
        "\n",
        "  display(js)\n",
        "\n",
        "def video_frame():\n",
        "  data = eval_js('stream_frame()')\n",
        "  return data\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "63rrUx4dMRD4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "#from IPython.display import clear_output\n",
        "# Get the webcam stream and forward it to python\n",
        "video_stream()\n",
        "\n",
        "while True:\n",
        "    frame_js = video_frame()\n",
        "    if not frame_js:\n",
        "        break\n",
        "    img = jsob_to_image(frame_js[\"img\"])\n",
        "    Y=(0.114*img[:,:,0]+0.587*img[:,:,1]+0.299*img[:,:,2])/255;\n",
        "\n",
        "    #Apply mid-tread for the encoder:\n",
        "    indices=np.round(Y/quantstufe)\n",
        "    Yrek=indices*quantstufe\n",
        "    #clear_output()\n",
        "    cv2_imshow(Yrek*255)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6Tq8vx6C1to6",
        "outputId": "49330153-8f9a-4c5e-dc29-70428e0f26e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    var video;\n",
              "    var div = null;\n",
              "    var stream;\n",
              "    var captureCanvas;\n",
              "    var imgElement;\n",
              "    var labelElement;\n",
              "\n",
              "    var pendingResolve = null;\n",
              "    var shutdown = false;\n",
              "\n",
              "    function removeDom() {\n",
              "       stream.getVideoTracks()[0].stop();\n",
              "       video.remove();\n",
              "       div.remove();\n",
              "       video = null;\n",
              "       div = null;\n",
              "       stream = null;\n",
              "       imgElement = null;\n",
              "       captureCanvas = null;\n",
              "       labelElement = null;\n",
              "    }\n",
              "\n",
              "    function onAnimationFrame() {\n",
              "      if (!shutdown) {\n",
              "        window.requestAnimationFrame(onAnimationFrame);\n",
              "      }\n",
              "      if (pendingResolve) {\n",
              "        var result = \"\";\n",
              "        if (!shutdown) {\n",
              "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
              "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
              "        }\n",
              "        var lp = pendingResolve;\n",
              "        pendingResolve = null;\n",
              "        lp(result);\n",
              "      }\n",
              "    }\n",
              "\n",
              "    async function createDom() {\n",
              "      if (div !== null) {\n",
              "        return stream;\n",
              "      }\n",
              "      div = document.createElement('div');\n",
              "      div.style.border = '2px solid black';\n",
              "      div.style.padding = '3px';\n",
              "      div.style.width = '100%';\n",
              "      div.style.maxWidth = '600px';\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "\n",
              "      video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      video.width = div.clientWidth - 6;\n",
              "      video.setAttribute('playsinline', '');\n",
              "      video.onclick = () => { shutdown = true; };\n",
              "      stream = await navigator.mediaDevices.getUserMedia(\n",
              "          {video: { facingMode: \"environment\"}});\n",
              "      div.appendChild(video);\n",
              "      imgElement = document.createElement('img');\n",
              "      imgElement.style.position = 'absolute';\n",
              "      imgElement.style.zIndex = 1;\n",
              "      imgElement.onclick = () => { shutdown = true; };\n",
              "      div.appendChild(imgElement);\n",
              "\n",
              "      const instruction = document.createElement('div');\n",
              "      instruction.innerHTML =\n",
              "          '<span style=\"blue: red; font-weight: bold;\">' +\n",
              "          'click here to stop the video</span>';\n",
              "      div.appendChild(instruction);\n",
              "      instruction.onclick = () => { shutdown = true; };\n",
              "\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "      captureCanvas = document.createElement('canvas');\n",
              "      captureCanvas.width = 640;\n",
              "      captureCanvas.height = 480;\n",
              "      window.requestAnimationFrame(onAnimationFrame);\n",
              "\n",
              "      return stream;\n",
              "    }\n",
              "    async function stream_frame() {\n",
              "      if (shutdown) {\n",
              "        removeDom();\n",
              "        shutdown = false;\n",
              "        return '';\n",
              "      }\n",
              "      var preCreate = Date.now();\n",
              "      stream = await createDom();\n",
              "\n",
              "      var preShow = Date.now();\n",
              "\n",
              "\n",
              "\n",
              "      var preCapture = Date.now();\n",
              "      var result = await new Promise(function(resolve, reject) {\n",
              "        pendingResolve = resolve;\n",
              "      });\n",
              "      shutdown = false;\n",
              "\n",
              "      return {'create': preShow - preCreate,\n",
              "              'show': preCapture - preShow,\n",
              "              'capture': Date.now() - preCapture,\n",
              "              'img': result};\n",
              "    }\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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4ieJKSDl8tlVbxnzj+QQCVnAJfOmaLNK6TQUCFsN8HbXcKlAZD4AQsBgmcnTcuBeUgse1FsLERwtkrwemdu1lYb4wBQU0X1Z32lTl7lORFihSdESXwXzphRHlPSDLNMJ1wMVaeLm7GxVrG966kH9ohJSBvhQpIGAJ8v6179AeFPk64IKFWIGjvRm9arHJCbhQCRYHwOf3lo17P5CCxcj3aGnzdmQFXKNq/gyApXsN/wgRsAC0gCWBgF1ZqosvCQiYi1gAhItEuBSXSf3D0s+rd/CPiAQFND8n2H/rlkLMllI2ohHQau2cz4nizruPWVPXnPmJblkgBUfYHgp8Nh4EzjH4dwUCfsCXcc+gDRAwABMRv9yzWsnQQ0pAW0GCA5EP/omDCPjiSLygBxDwAX/cEygSAFsM0DePpIDz383pyz4k4CYgAv5gJnKeGUHbPdharbTlwicAAu7s7Y5XL/F5B7ZA/MvrVn5+mksWYgoISPS7yQu/Rqvw64X3+4UC/ZbuDFRgihAScOqS+7i5Wt7IgdxWCHtfroVgBJzzFP68tR97XgFRFh8PyPw5vK/3wwIX1HzlOuA7+LUP4MHaotuHe82XQ+pYVkBP6q0/9uVd0at5dyIj4GzF5634eXaibUq8L93NWYeuZcEImHdH8RiPJwtWLHo9+YgWbISEmx3vtwtuORm1yL9Mefm+W1fjsFYEzJtTlPPRGoOahD43SYtGTsDxqzCRmBDYePdt4nOKR/Ca3XtzzheNim0KB5eJgLI1vwp23S5b46idhMMruIaAFfbFmhXvf7d6dmEJjkdXUETAsecEJ9oX2Pjod/eAlvGkmuCAhwY4dtsDTUbFfAQsD34VncrfX/XJ2syQ7Z7n4yooJuAgNaw7fK9qNRlS5SMhC3cbv8/n3OZOK8zDcgTc9TsOdfYt7fudUw3d4K0V73hQBe0KyEyV98FI7FHZVnFf0ae82+efbzRiT7/DDnWwKiDvcy0ivXhxogs4ntSVtdTbIhtb4YjJnQ82br++PCQEHGqHiH4XPLLuGZTxbtIXP7XtO4PT3dc4lIWSEXCUfeLbRZnEQ+2bDZdyP9PgR0abJfw4JUaZB2UvBXPhCf7+xv5OWp/K+4AGkzfru+m2Oajam0H2BPSMcm57tD8PYmDdj3dVgtEI8hHJCah+Tm8c4w2uxZtkYPh4FI1gyFp3L97XnAeIgqYiYGC4S0q3MFGglyLZIH8cSx8c0wfHt+qx/jUSQwJes29GrxzRUV3zHYmoPr8DqH0o07jnhu0aieaGC4yIHmQoQtkQYOfo9K/k+1frinty1FEcPW0lAn6POfAlwmMe5FY12j71XqpALS6+BK3Yebszg9KJIjQnRLui8wp/8Rm/R2fhR8EzufgNouN3T0g4tCrlxx9/dcNEBKx4fOp+Hdexb5xex6DARxzut85noekEQQMCpk7yfX7gd7id/xuJxyPuaUpTWDuHEOl0SE7/wOrSAaf3aPOxjMji0+qIQ/UHhlEIgvUCqrby0vW7KeDtcLl1kZVtTfjr4xkYvEjT94BK3R9QqRadMc37kgcD/wdE1NtBIQF1/Atd+AhEomPgKJPnklTxLiQerLSBid2OfP6slUbM2wgJZ9/v0uOEz7Qh3hbpt1WRlNUvCsr0AyoEwOJ7nwQG+R1vZy/1uh3jCFZPp6sjk0bAZ+mcVzOieG6EfwRFThyDel1aZqgYry3yQZcwOKWAz+y79+ZdZr8FC479EdBdF5BOybDXmRTs0C9TnYIVssoz+76T50d3yGtKWuSLUV9erZmcz49P60w83/0B35W/13XfhEsT/rdz5fAenPKK4JhutjVQLAX3Kjz+VdgC6Sx7S6o2XTBJjelf47qgSAR8P9i5GWf4u67w5mGdTpmnu+/YxIe/+jvC+/rnnMtYY7tEXBsB+5Ya+6tr95ddL6n7bCtav0r4u1wMj3WUtypVqX7ALm27n3/u9uv3dtr1hvAKcr/w7tFJG5swWrbleL9qoyM80bXgSwmdx89dhjIf7dvyg9uxh8TJVRsEeG2MryTadMnM0wr2+bdVZe6DkqtKKTc0eQ4Up4ms7dyNdz3Wu3ktTlCpO6S2Ls9A7e9RKJX+FXzXHyqSVjZcj/SeQn7NvNcWNqgITnIlJOjf7dKawiH1aJQ8QnGciyJ8+fl702ObfBquFLBPIklr42gcztDIw7RyOeytKEWZOOquYvHrxRVpBSvrgJ0av888e6N0GIsMnqkk6Z1m9a1hob12RyEnLE+2oCeIgL6RL0EGqNu/x7t+MMD2ZiNaE2xeB/w+X+K78kq/o/tHRNt2jNfK+OBIwkkFKGmgqID5JR7/RiDh9qJQouiFBRkEHc9qyYkl/vFbwcn+DRhu2odAxZ1+COsur8//JQyQ48EF9HVyBIbUj2dg0y1qFmITJzrTvv5bO+rymt31Q75ZYNsbf/5VbGvY65qFxpYTWCl//E94C6Lb0r6msHUaN+lKTJ3aIEObCJjSttg/djw88h62HXmv69fPHe9C2010l1SnFvaldrEmBXtjdVbt+9rz9B4l/1rMQJcONjxHoal77vVCFZGjUSHg88qN912xxSctu+uBud7eiN1R59nZqqpyp4yru39wE0Sq3VUp2HP5Rop3+BvlwRYnP/2IHL0GhbPsNo+291KM2QoOXIX8RKMZ7Dyv9r+PSoTIZn21uyZHSkDRw+9pfQguXZA9CPq2br8DsOPqshl030lkMIXkrDgmoUqP37/4XB+FJO0oWPy/e1DziJ2UctSVutSQ/HND6sd3FB+s7v6lDcTlCgW/F6/utcAGjDYk3zs3JtDZrk3yQPDEUfr+bw5P3Tb+D5gquB9CdEosAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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Sj6Ip5n7QzjipG0TAcWLsFuRj1kOc+/zXfAaMnpAJBnZx/frXegL7QMBx4luRZlHa/XPhXgiBNgv21QoT46eW2rvGsx+7GkAZcJDTONImBd8v8lbNfQACjnLIflNH0Id+Z4gFdJO8/XHs5aibBGoFEXCI2N1Q4qNg3A+hgC6TuFPEgQBoPFkXblhtmI0ZsPXURBY8wqB/4A4EHCBWfgN90Aro417kMuCHT+4jYUZABKQBOy4MAgG7yY1aDk3FvGIc/G2Zma7/6LgwldAPRrCeZPRdaSmE9zTMa9pZT8tAKaCDxFrAN9VOY1sdrQ+D4VidjAXAvqfTxWykNxCwD8LAlLJD+n2NaSMW0HK20Tn29PrufOkuXf/gDzIBTSdgDPFVSciu2DIwNcl0anWBLPiB18Sj1DPk7xHo94NcQEuJ+571RracS4qmkocERMACnyH3pJuqQr8bEDDHe8mDls4NODUHpYBGKsH3jPfwH0AMIuCF+F19N/OvKtU1TTOR8v1y5yGUSkAbyVixj/xMp59sJN8I1BFQc0rG/GprL0NIs9/bwfxaiCz4zWedjcIuWlX/emsihYO5zJMhYAg/+0JIkbTdpXC2h397UhADUkOI8TfEpdu/VkV7VPa0egdRBExBayNFLF930wc6jidoTIGG1w0s/aYU5xEw/l2iTSr8XD7CwEkJX6Uez2XATPAbDDsttRD0mWQhFlBPImez3tltCLNR67r3ZVtom7kUTdUYpxEwX8xfe9tiT1F58lL0KOhRwFK9I9sCTcE3MsZ9WxBqUZCmEqKoEhxv9Y7iK3uO+vqWveex8DPZyUuHUHFHnEXASqPLSpj2XtUQBT0J+GRfvp56rUNk+c5lK9Rsug2cMud3NvntiW4EHI99Tz1npzucMW2k/YXQGuFhkLYhWuxHPfa29b6V7EXNTK8Ic36/6LKghwjYGvxIHh+C4h7BdZyvQnIQNC/gcNY7KhJThePC5SrkKkgioNxWGI5a783A3rsf04K6g1QFTUdAHS1hd9bUXWXWiO0KuC343dd9GTnxubVnkSkCHSQVUNCHGwh+t6t/54RPR3rvwFAbGNg8sDWRDZopXXh+3gsfNiMgRfRLXfFi4nyHZdpoI1QlEssJhRYFPMwvau/9z83bHb5H7ZnwaZk26jJDfaayDAf//c0f41sJlvCBftHvFcLab2rh6p8PcFvrr+1tx3fFy+9UPM2lpz3bCOYiYDzq1xFUqO9Fagu+8dKEtXfctIAoaEzAwy6WsWup7/n7cFWn8Yhrq+rPIym42wcpBeR+mI657/tqWpvI368rLuCil+c4zKugpQh4mN3bN1syFTOjKf+aBnJJgHO+FNFoGAEpHf9KQ4+fkvedVWeaDntnBnf8axspPY+uYewyMjMvuJaG9fQvFxXj43vpWaBCy0dgM5BAQAmP+cNwv9olpmIWFBsXSb18b0NCqh3hCoImyoDPHR/FcmCq/LfVkZ+/Hd3C1Qsip6U4ylMZsZAFj492ph06nLuDYgJdUyxnCIOEEZAprdsSLRuKPle8MtlljE8NobFSvr1pmiYC8iXy8EObDin9y0IzJ+i5mLFLObL07rfNNdkbBnVnweW5Ro9JHQ93g670U5iV2czq6NOo4OKrODIvIF8ppzLVrVYYS+fw9zrU5XsXKftj9W/C2VgY1FsLrlV9sx68B+zEWGv6X9EpoKZH5MiuwqBaAatNL9nUS683kW/YdomlZfm1saVdhk7AvTnNWMNzCiGyNHe1GSgus96QVjorIfcyyiUMle53jPcRMpGux62vUDDymmk6T7K8MKgxC77nvukaY0p9a6nw/0SWSxaDb8uYHIksjoLTEbB1xB0dz1W0UmNDtRFiYUtMx/ElFhaXXpO+LDij3/kh6F7ap/b65gmV75/Hb1ZqPN0ADZlD/f0LFdSWBZfT4pPH9SfwaUpv75tTvEwJTqWWnEprzKFPcE070Czr8mFlAub9O8zsGUin01u67/+tOFobl1hvORLNKgXJsuAdRehD6S+ldM5qv3MjJ/OL6RExNc1KiRSfX8LPmkdEUxnw6N/h6+9PKREUV5aGogYD10BxgiVFwdmJ6d+i8/In99j4chm+8it2zVxJdnJ4W5qnym+ls9SOQF8UjJckG4X8NtOUATd0NdUev0894N3bu51zJI5VeUq7KX1ZnJSzRQzq5CWJgOs5tz3fQlS6/jh2jnBfkGisC60zBuZC7qqEnfab+MJ0lAHPbc+XDC8d/ZuCJm2fjtKi6/Qy5esgjtAqmmGqUy4jaRPuQGHiuqbfY2Pe5Rz9EbefX9k4TZ+HNhdWIOBNv3OTFFX4mwiA3V0v3XPlREFqIKWA1KmXXqP36gNf3qce7we5HHfPkJRzhX7PCDE6b4pHGjBgUsClCRdDZuSLuMLR0AWduuUOeXYa7s95OB9tcCA8mvgsuNG/hWWnKoXreY42p/3cMhP0FiByz3bhteB79YNjq+ky0zn+9GF6TiexhCk7At5TTGIaDlIaHLvoZELnpVBGwJRalgJrP1qPben0jR0p13GBzEG6zzcXAS8zMb5fKKDr+l5Y9onpUIeNSax5H+Ih7M6kb/vuA0+QZcHkAZ7ugKukSCEelqI+Bv9dk2oNMNcXTDTEIsfj6OHs66lv+/PnehfuL8Fvy/igTm4tpSIKhTQRUEr447jnd/u+/1h3OSOLLdxyTRErNkwJuCyBBSRMK/mCX3qXh8lqZJc0mZz7JAiRzTAjycvTypBC4Za+L4dKQZLj3JNo9WToBogGpL6/03yezFGacovtD3et1vu5t1T1EarnS9qsO9oIOP3hYij49/yw7s9bqq0ux75eikv7jZWVVz6Z+YD/B0woss5WmEIsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So all pixels get the same quantization step size.\n",
        "The de-quantization (in the decoder) is then the mid-tread formula for the decoding side:"
      ],
      "metadata": {
        "id": "4zaGFT-Z15Ks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Yrek=indices*quantstufe"
      ],
      "metadata": {
        "id": "fBz9vTX117YV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calling** the program:\n",
        "\n",
        "*python videorecprocyquant.py*\n",
        "\n",
        "**We see: The fewer bits** per pixel we use, **the fewer brightness levels** we have and the more visible the light jumps become."
      ],
      "metadata": {
        "id": "75lFBW80189G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Python example for quantization of the 8x8 DCT coefficients:**\n",
        "In this example we quantize all coefficients of the **8x8 DCT**. Because of the Parseval theorem we have to **adjust the range of values**. It can happen that the total signal energy of all 64 pixels of a block is concentrated in the DC coefficient, e. g. when all pixels are equal. Then this coefficient contains 64 times the energy. The root of it is the factor for our signal is the factor 8. It is beneficial to observe the values with real pictures and then adjust them accordingly. There, the value range from 0 to 5 can be obtained for the DC coefficients, and the value range -0.5... 0.5 for the other (AC) coefficients.\n",
        "\n",
        "In python:"
      ],
      "metadata": {
        "id": "ttOWW1PT2Ivi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.fftpack as sft\n",
        "N=8\n",
        "bits=2 #for DC coefficients\n",
        "#resulting quantization step size for 2^bits steps:\n",
        "#Steps for different spatial frequencies:\n",
        "#DC indices with range 0...5:\n",
        "quantstufeDC=5.0/(2**bits-1)\n",
        "print(\"quantstufeDC=\", quantstufeDC)\n",
        "#Two AC coefficients, with range 0.5-(-0.5)\n",
        "bits=2 #for AC coefficients\n",
        "quantstufeAC=1.0/(2**bits-1)\n",
        "print(\"quantstufeAC=\", quantstufeAC)"
      ],
      "metadata": {
        "id": "tIe-ZBWP2RBZ",
        "outputId": "bd70c5a8-2d48-4e2f-a49a-66aad6350af2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quantstufeDC= 1.6666666666666667\n",
            "quantstufeAC= 0.3333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculation of the indices for the DC coefficients, every 8th coefficient horizontally and vertically, and the AC coefficients of the DCT, in the video loop:"
      ],
      "metadata": {
        "id": "GQundWjZ2Tpr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "#from IPython.display import clear_output\n",
        "# Get the webcam stream and forward it to python\n",
        "video_stream()\n",
        "\n",
        "while True:\n",
        "    frame_js = video_frame()\n",
        "    if not frame_js:\n",
        "        break\n",
        "    img = jsob_to_image(frame_js[\"img\"])\n",
        "    [r,c,d]=img.shape\n",
        "\n",
        "    #Encoder:\n",
        "    #First reshape green frame as frame with rows of width 8, (rows: order= 'C' ),\n",
        "    #and apply DCT to each row of length 8 of all blocks:\n",
        "    frame=np.reshape(img[:,:,1],(-1,8), order='C')\n",
        "    X=sft.dct(frame/255.0,axis=1,norm='ortho')\n",
        "\n",
        "    #shape it back to original shape:\n",
        "    X=np.reshape(X,(-1,c), order='C')\n",
        "    #Shape frame with columns of hight 8 by using transposition .T:\n",
        "    X=np.reshape(X.T,(-1,8), order='C')\n",
        "    X=sft.dct(X,axis=1,norm='ortho')\n",
        "\n",
        "    #shape it back to original shape:\n",
        "    X=(np.reshape(X,(-1,r), order='C')).T\n",
        "\n",
        "    indices00=np.round(X[0::N,0::N]/quantstufeDC)\n",
        "\n",
        "    #For 2 AC coefficients (the others are omitted for the sake of clarity):\n",
        "\n",
        "    indices01=np.round(X[0::N,1::N]/quantstufeAC)\n",
        "    indices10=np.round(X[1::N,0::N]/quantstufeAC)\n",
        "\n",
        "    #De-quantization:\n",
        "    #de-quantization in the decoder:\n",
        "    Xrek=np.zeros((r,c));\n",
        "    #DC values de-quantization:\n",
        "    Xrek[0::N,0::N]=indices00*quantstufeDC\n",
        "    #2 AC values de-quantization:\n",
        "    Xrek[0::N,1::N]=indices01*quantstufeAC\n",
        "    Xrek[1::N,0::N]=indices10*quantstufeAC\n",
        "\n",
        "    #The rest of the DCT coefficients is not transmitted and set to zero.\n",
        "\n",
        "    #Decoder:\n",
        "    #Inverse 2D DCT,\n",
        "    #Rows:\n",
        "    X=np.reshape(Xrek,(-1,8), order='C')\n",
        "    X=sft.idct(X,axis=1,norm='ortho')\n",
        "    #shape it back to original shape:\n",
        "    X=np.reshape(X,(-1,c), order='C')\n",
        "    #Shape frame with columns of hight 8 (columns: order='F' convention):\n",
        "    X=np.reshape(X.T,(-1,8), order='C')\n",
        "    x=sft.idct(X,axis=1,norm='ortho')\n",
        "    #shape it back to original shape:\n",
        "    x=(np.reshape(x,(-1,r), order='C')).T\n",
        "    #'De-Quantizer and Inverse 2D DCT, 2 bits/ transmitted Coeff '\n",
        "    cv2_imshow(x*255)"
      ],
      "metadata": {
        "id": "_ChvUydy2WOB",
        "outputId": "393da4fc-fc85-477b-a32a-f7397a011435",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    var video;\n",
              "    var div = null;\n",
              "    var stream;\n",
              "    var captureCanvas;\n",
              "    var imgElement;\n",
              "    var labelElement;\n",
              "\n",
              "    var pendingResolve = null;\n",
              "    var shutdown = false;\n",
              "\n",
              "    function removeDom() {\n",
              "       stream.getVideoTracks()[0].stop();\n",
              "       video.remove();\n",
              "       div.remove();\n",
              "       video = null;\n",
              "       div = null;\n",
              "       stream = null;\n",
              "       imgElement = null;\n",
              "       captureCanvas = null;\n",
              "       labelElement = null;\n",
              "    }\n",
              "\n",
              "    function onAnimationFrame() {\n",
              "      if (!shutdown) {\n",
              "        window.requestAnimationFrame(onAnimationFrame);\n",
              "      }\n",
              "      if (pendingResolve) {\n",
              "        var result = \"\";\n",
              "        if (!shutdown) {\n",
              "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
              "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
              "        }\n",
              "        var lp = pendingResolve;\n",
              "        pendingResolve = null;\n",
              "        lp(result);\n",
              "      }\n",
              "    }\n",
              "\n",
              "    async function createDom() {\n",
              "      if (div !== null) {\n",
              "        return stream;\n",
              "      }\n",
              "      div = document.createElement('div');\n",
              "      div.style.border = '2px solid black';\n",
              "      div.style.padding = '3px';\n",
              "      div.style.width = '100%';\n",
              "      div.style.maxWidth = '600px';\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "\n",
              "      video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      video.width = div.clientWidth - 6;\n",
              "      video.setAttribute('playsinline', '');\n",
              "      video.onclick = () => { shutdown = true; };\n",
              "      stream = await navigator.mediaDevices.getUserMedia(\n",
              "          {video: { facingMode: \"environment\"}});\n",
              "      div.appendChild(video);\n",
              "      imgElement = document.createElement('img');\n",
              "      imgElement.style.position = 'absolute';\n",
              "      imgElement.style.zIndex = 1;\n",
              "      imgElement.onclick = () => { shutdown = true; };\n",
              "      div.appendChild(imgElement);\n",
              "\n",
              "      const instruction = document.createElement('div');\n",
              "      instruction.innerHTML =\n",
              "          '<span style=\"blue: red; font-weight: bold;\">' +\n",
              "          'click here to stop the video</span>';\n",
              "      div.appendChild(instruction);\n",
              "      instruction.onclick = () => { shutdown = true; };\n",
              "\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "      captureCanvas = document.createElement('canvas');\n",
              "      captureCanvas.width = 640;\n",
              "      captureCanvas.height = 480;\n",
              "      window.requestAnimationFrame(onAnimationFrame);\n",
              "\n",
              "      return stream;\n",
              "    }\n",
              "    async function stream_frame() {\n",
              "      if (shutdown) {\n",
              "        removeDom();\n",
              "        shutdown = false;\n",
              "        return '';\n",
              "      }\n",
              "      var preCreate = Date.now();\n",
              "      stream = await createDom();\n",
              "\n",
              "      var preShow = Date.now();\n",
              "\n",
              "\n",
              "\n",
              "      var preCapture = Date.now();\n",
              "      var result = await new Promise(function(resolve, reject) {\n",
              "        pendingResolve = resolve;\n",
              "      });\n",
              "      shutdown = false;\n",
              "\n",
              "      return {'create': preShow - preCreate,\n",
              "              'show': preCapture - preShow,\n",
              "              'capture': Date.now() - preCapture,\n",
              "              'img': result};\n",
              "    }\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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Ae+L7drGV4R9vqT6honXrYvl1S80T7PNWqjTVsp7v+869/q1bZLyrH0j1tt0eHEC6HpSP+NKLr3xx73vHDFrAmUH/WFPvP56PeCn/bAhwZpDwtm4byke8lPnDEODMoMEHtXChfMRLmT8MAc4M8v9otEH5iH0FLmX+MAQ4M0hYO7elfMS+vUOfO/Y8kBDgzKC+HQnQBvIRow8IRqHu+9298mg+4sWMgsnHisXlia55TPF6f3bzet80fP69l/fD9w3v+7JVdWgP/TjAJYAWcCac3D/WAQHOhFO9EKd63rPj2PL8xgICnAlNAlxcFIIDApwJofUCCQgQjMqpXohTPe/ZgVHwQPw8wRTftlT8+bhNeUJCcZeUL5i+/87/ouPFQtdbPNU/3mxAvmDACvIFA1aQLxiwgnzBgBXkCwasIF8wYAX5giPxwz2f6pjXq+vKe2//Tct5wR/dlvKGhPw/Yqm+Km7BA+kbmXyyTZ4HBDiQvnMzUPEHUA8D6Ts/91Tj/3wgwIEUzR95FAjwAAQ4kL4CbIr/OxUgwIGgDzgM1MNAMAoeRrx4QJcXZKl5gkP45/kvt9cXv11cFW8b/ECK/3vfUF/+OoBLWReQQAs4cx6slMpSivGAAGfOg3whLKUYDwhw5jzIF8JSivGAAGfOg3wh3v6xAwHOHH8dwKWsC0hAgDMHfUDACkbBLaG8IEvPExzib5tXX7/++uXmp5tf/ufj//xy8036/OVXL8/WdKv012FsWh+Q8OvxQ+Bz3wdenztoASOhtVJaab1t/uijnOqTEQgwElm2ybJNlvV9QnGqF+JUzzs6+SbP82d578HpqYZnQYCRONvkZ/nZWXQBLn16DQQYiXyT53nevwUMxQdCgKAVWZZlmyzrnW7gVC/EqZ53dJTSWmnVuz4xCh7IqeQJ9vmL2jw7yzP9JyGE+Py5fv1yo57L7Pmmbch+0+iZfMJj9ftCoAUciNKHpm/s31lqCwkBDmSjsizbZKPPMVrqhVrqeU3GM53leZ6NXo9L9QkhwIGc6SzP881kAlyaLQMBDiRXmzzPx78F0w9AgOALMp1lm2z8FnCpF2qp5zUZWh3CYMb+HYyCGziVPMGU1+Ovbv/1+kxu5HpN77/ZPtuc/ePftz+nm8Turgr/2RzlC2nKC+KzlHnAPmgBO0J5PWhQoA9GYF2P7u3F/xFjAQF2hPJ6PHf72TrLsmxTuyRKK6WUXtrUjdGAADtCeT1euv18ned5/qy+BaeVTnUKAbYFAuwI5fX4ivbTs7Ozs7N7AkzTVKdLc0tGAwLsCOX1qOOu1nn+LM/XO3rfpBotYHsgwI5QXo9nbr9Ks2yTZbUAlVJKq/5hWacGKqojlNeD7rnFwQisfcDkMExeysoZozNYgEvNE0x+3y9unb/vvPffSKXT1Pme+zuX7r+Kq4vfLq5v30qVFn+/kn9wrzflDWmbL2RpoAUMQH7fxgnQf59aQj/yNvF8QIxGngYCDEB+3yupUp0+fMKjpFJaKelHPKtKK6VUrTuMRp4GAgxAft8Ldyv2WzIaDfsC1FanOtV72l/aen6xgQADkN/3xm3/98H7j9+a032apuld+OjS1vOLDQQYgFq4r91g46EAlX7s1qz3WmudPmgBIcDHgQADkN/3MtDSKam0Uo8I0Cql7/UBSYC9JwwvHAgwAI1yN64v+Mj7j0a9SJlImdy9fjLzU3syWIBLyRNMft+Pbvtv7vUP5HO6118n6pD/wxaPTvl9bXb/cH2zM6nZfr7+dVd+cqNornX9yH+c63qNaAEd5PeFfL++WGGrxPK1g3MPTIQAHeT3XQV8v76UiVjJFZ8O5n6B516+yfid8/t2bhvruGVlxUrs2QQ49/nEEKCD/L4i8tyWQloh9pbNhZn7fGII0EF+3+39IIMIlHIvhNizXf+5zyeGAB3k91257XWk4xopVlKiDxhi7uWbDPL7aBtLgJWw1lZ8Y9HFj4KPLU/wX90ttnAt3R/vbrnVdivExm0PcY6m853rU2WqZKMrURXXv11c337n9Smb5vfGigukedr+783NFzy5FlBJpbVSMnECDMX1DeVw1Pk9EZlbi3hyAsyczbJ2fl8orm8o/jPhucQFzu2Cz608o5O7lu+VE2Iorm8o2qazjAucmy94cgI88+L8QnF9Q0n3qU7Tu7CsucQFzs0XPDkB5u7WS77fPhDXNxS91+n9uMC55Pmdmy94cgLMXMtHvt+nQFzfUJRVs5wbMrcLPrfyjI5KDoGmtd830mpWiUy+OC5GwY8zWIBzzxNM83v/prP8LM/Un+7eqrZbIUQgrk8kyVoIIT7YsixKU31DvprKZLLJVGie7yEvsBAblajN+c78h3v924Av995th67/95M1RVEaS/7f78Xj8Zn+7/zott8M/P2+LL4FrOf36myz2bRfzZ6unRRJJaUUVtiqStq3Y4f1EZpb1lgto7WVtdZ27tqNvrRrA4sXYD2/t+Ni4tRnU1bY/V7YMjk80121/V1hlGqxcm+svqGpjDGm7Kxn7rkqixcgze/9WmX5WQcBkm+XVishxMoe4vps0tZH06uqLHRzgxTLHyysKcrCdBZgHun3+7J4Adbr+XXM50Edw7SSKyFWtpB7IfbtBZhKW6albWwwY/mDhXsW3/U4Z145pmbxAqzn97qkgp+bvyKEuGuZdCLFXsjqpmNcX7qqtD40n08Syx8s3SCkqwBpmTkIcCTq+b1uFNxWgPf7gHYvpJFiteoQ16f2qlX21mh9QFsaY0zVtU9Hwr+IVI6uLF6A9fze3qNgmSSV7BzXd/jV5mFztFHwYRDc+XDcAhj8+3PNE0y+3xvX9zuvTHn5q6l+dvN8z+mZsPs8+Zfv3Pvfu/m/wosLfKGz/OzZpvFOd2l2N9c3u/JfD1vz7p4/d5++vukLt/3Z+ZTkWzatQ+hD5//GHYeON5UvyP0HGA1q8bRSX+TxIHxfr23n7tDOtG8KEyu/WCkhFlTy0hpjyu6jXx/yO2k7FYsVIPl+mdpkWfbQfiFfj/bb9sWMUKUxpq0d2Lov2BUq784NPoYej/xO1+ed7E62WAHWvp/K8jzP9NZ7n3y9er/lcYt9ossy2Td/8oBe2bLQzaPhrlB5b5z/N3QUTX4nbQcerjWLFeBr1/e7dn1BX4Dk69X7LY9b7qUutGwtwFRWaRs/sCtU3mvn/w0X4MHv1IlcCRF9hkKQxQqQfL+9zvL82eaBAPz5uh1awLIs2reA6aoq9QgtIAnwsxs0/W7g8cjvTO1K7MWDBWFHY7ECJN/vk8qy7KH+hO/rte4DrowpTfsHaGo/Tuo4OuLWtYBDj0d+p7Jib/fTRW0tVoA0ClZKa/VI++P7eq1HwdXBcWtbjrGyZ1IBjC3L0nSfPupDfqcUUiYTzmMeHg84kzzBf3G33F8OPp16I4S4vhbiXIjt/f6fH/9G6/Z9e28/STJxf73DJLnLh/LmMLgJxgMSh/nByv7ofMDQ+on+/N26PgPzrMn/o/i/c+97objDJj66LfmCwvmBND/5m5H8wcW0gIe0vVqt1XqdrtPadqFrTqMN/58ydmcntg9ILR/F/401yYn8QO3tx2YxAtyoLMs2mT5zfT96nfpKdKF8Q3DsuRqxfUAqL8X/jSVA8gWze/tj+IOLEeAzneV5ninpxf3RcIFyu/kCHHu+bmwfkMpL8X/rJz/dH/IDc28/9u8sRoBnzvfbuD4gve77e/682LHDkGL7gFTeItLoNwT5ghQvOJY/uBgB5u7Rm3S3YDLq/BbOnxc7dgsY2wckAZaRHsGFIF+Q4gXH8gcXI8BMZ9km26i1Gw3TKNDv4/knPLYAY/uAdR/Qxf9FO7AH+YL0hEWO5A8uRoCHtL1Kr1W6Xq/XD0bBhN9pH/uZU2wfsB4F94z/awv5gncCGccfHL4+oFvq4qPgyRP8n+nzl1+9PFv/ycXt/Wyur38Wd+vsfXRLcLx38XnnbkUEf5088rtoHjHt983j8b0rz4f1+tl5aart7fXFbxdXQrjy3I9DrKo7f08E6vOF6/Nt6dbr4v986Hih9QF9/POk+njgCzr+7H7fz5/cl8EC5M6DsUk3m81mU1+MyttSRLT/vdB/meYRxzqjOs7ucOAqFFldt2wN9Tm2/9d0T1dW7PcR7ZjBAuTOg/Fyffbq5auzWoDG29L6f/73QvMzKQdcrNHxXZzdYa0YY8VKrh5OgqPyNtXn2P5fU291Y8tEJvH6nsMFyJwH4+v12cuvXj6vZ0uW3pZmxfnfCwlQu1t0LAHWcXb7VKeptqFV8+tyN9Tn2P5f06Asr0ypy+4T4EMMFiB3HozX6+cvX91rAQtvG8zr67a+EtJAcsK+kJ/m8gjvC2mFEPaBwOpyN9Tn2P5f0zzlPPLvD28BmfNgUAtIAQd+CxjM6+u2j2ZCj7heIPlp2upUa7139RUUYFN9ju3/NeU3zq0ptDbRev2DBcidB+NV+vzlq5dna1+A1Jd5Flj/L3Ti2q0lE6t8d3F2SimlbaiPV/ddG+pzbP+vKb9xZk2SSGPbzq9uYrAAufNguFFwvU//zHuj4Ed9uNA/3C1qFe2EyE+j5fitkI8uYnVX7qfrc2z/r+nA2q72dr/iH4S8p3g15zd9pG3g87Hy4pJ/R898vxFCiIuLuw+4Ph/5WeSLUXwefayO/6N5wvSGt17g/XwdSdK87sA/O7/xv139UPzhL0Kcn/+TEJeHQUQR8t9oW/uCVM4G/4+472/ejz+k44V8wba+J/3V37n4wKHrQUZ/EhL678ZqUqjvttbrdbpeKz/ez2fqzunW7Lbb7a6kUHlSPJ3/2iar1SqxTZNK/Jaorf8XqufoeVDqecTDiC7AYN8q0vGpDaK4Pz/ez2fqtZkvyt3N9eet2boWovYj3fbMCfOq4Th+udv6f6H6j10PFB84VEDRBRhaviyWAOkf98oFHfzm9kP3panzc/yf2d7c3OzKrfPrqD5ou3G3/CYB+uVu6/+F6j92PdT+5sDjTCbAWAlSqAV45fqAPzV8fuplx34uD3NAqAWkZ6l0/q+ckEJ9ZcIvd1v/L+Rvxs5TQvGBsxNg8AlDpONTJ/iVyvKzfNX4z568BSx3Nzc3W0NBA1+51ymy+FXVTki+ANv6fyF/M3aeEooPHHpdJ+sDxvqhug+oNnkumvs2U/cB/+5GwTsntEf6gEWbLJwP+oAt/b8J+4B7uxdydn3AYJRJpONTgddqnQrRPMrlGgWTAB+Mgiu5WrUItnkwCm7p/003Cj7EB7KNgikviNBZkmR3czD+MLBAhO+D/cWNeksXT/fClr8WhbE0T5Xyc9TxcLS+n+Nn73j0PvmBbfPzvkhUUm3L+riUJ/m3Q7zfA3+P+Kvrs24qoxKl7Pe0rh+tyxc479q3C/h/FPf32sUxUt/S9+do/y0tZn4v7rAPfpwgHf+9/8EGorWA/hOIoYRuGXV+376JMSJD5bgtbm9vb4vbtt8LrcfXt6VKWoZeW1dgETmOk65D11t9NAH6cXhD2QVep/g+Y40pzV0fiwsqx/Xt9fX19VXrQXfto3nzbfueD+VDabqgdV8ychxnvVZNx+9FE6AfhTKU0H+T4vvKypRlWVruPLxUjovb679fXFy1bgFD6/H1PR/Kh9JkixTWlGVhrIgcx0nl7hqkEE2AfhzeUEK3VorvK1zfiTsPL5WD+oBtv0c+mj/f1j9A2/OifChN+YBLsoEix3HW6xV2/F50AcZqkUJNOcX3Xbt/clP82thQi3Jxe31xcXF1+6rl98hH8+fb+gJs69vRnaEpH3BBfmLkOM56xdaO34veB4wlwCf6gFod+oClmUcfsDTG2Kvi6urq6rq1AGsfzZtv2/d8KB9Kcx/QGGOMFZHjOOm6h65biOij4FjjqpCQabBHo7kZjIJtZa0tbovb4va2wyiYfLQv59v2rT9yB5pHwWQexI3jrFft7/i9/wc/h14gJrSvrgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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ztdVuV07NBySu9Wqz2azVtfu+wvlyP8cPFUIIcbxOYLYW4vdCCGHfi6dUwJ33lXg4jm+odR3HBo/gjrgGTN75ebaqsuZPVFonkB5BpCXjtnImPiEE2BEjTGmMWRz8vEwsPpGP5v7x3jqBJLyd2+Yz8QkhwI4UdabLMqsPfl5lxUI0D2rx1wks3ZaavMVMfEIIsCNFLXWhpT34ebIWom4uQH+dQFqg8pBHeCY+IQTYkVJkZaGz+uDnyVoIUTd+ZPrrBFILuHXbi5n4hBBgR4xQxpjyzs+TYrFo1Qf8eJ1AEuDuaP8cfEIIsCN3i96Tn2etbTGP1/cBSWllYP9USS5Amn96PZE8wSFeO9/v+0Mf8ON5vSGuDuWXSyHu4gQpfpLKbb3jTnV9v76gBWQmlEF9sn+5HhAgM/4NoNfGLzhRIEBm/OXa6HXpF5woECAzIQGSLzj16ZoQIDP+hGJ6feO2J7ieUFIgQGbQBwSsYBScmOuJ5gcJsc/7sTqsg/j8U4WFEH9yft61yxP87QPnE+JufcDLI3/wFNf36wtaQCZCeYKlt506ECATKpAjjgYhc7kxc/meJ4cOpGklATZN53DuQIBM5IG+cu5tpw4EyITOlNZaBx/BTfOJnDsQIBN6v7LgPQEqbzt15vI9T479GPj+KJg6hRgFt+TKm8NwqnkpUuHPy6U8IH6+D7ouFMdH/l7ILzzEEx7F/035OqIFTETTXzDm0rI1BQJMBA0aPkTK4YJ/DK5HIpoKcC72SlMgwEQ0zgM8aC3ODwgwEY3zAA9ai/MDAkxE4zzAg9bi/MD1SETT0HmMgj8muQCnsm5dU156r78VH39/ihcknjifUDh/8LV7/6F8H1P2/wi0gD3pG7k8l8jnEBBgT/rO3ZjL3I8QEGBP+s7fncv83xAQYE+KeJFGx099/m8ICLAnqQQ4g/HGg0CAPUEfsB8QYE8wCu5HMgHSfNenE13HLgR9z2J58eiLR5/nwlvn76HyVXWXJ+RpqKCD/MHLyP5zBS1gIlyEc1a6pVNjv3gc1lZteP6ptpQQYCKUUkpppbYuv8cyUp7yhDTtA061rwgBJiLXuc611pQ3JCZAyhPS1Aecql8IASYidzTNA0x5QmI2DvmDoXLn7h9CgInQudZa57ppHmDKExITIPmDoXLn7h9CgInY9wCVbprfg/KEoA8IkiBlJmUmZdP8HlQOo2CQhCd6tclFVbw22/c3t7vym0C5l+STenlCfHwflV5fB/afKxBgIrRSSmvV+nq2jZCeWkQ1BJiIlVqvVqtV6zlHbW/A1G7Y1L4PGxu12mw27QXYdprm1KZ1QoCJ2OjV5mKzan09Q4IKjWLyyP5zAwJMxEavNpvP1ou2x4WazJDAdGT/uQEBJmKtVqtVe/2hD8hdgamglNaqvf4wCk51oqmvBxjiL+vHX3391aP1VVX+8tfSVFcfxIdq967w1wkkKL/wK/ea8oS8EM049/g/n6n9QY2O1kpppbHkS0cgwJ6sVuvVar1axUuCh0AfsCeb9Waz+Wwzw85HGiDAnlysNxebiwsIsCN4BPdk4+Cux7kCAfZktVqt1iv0AbsCAfZEKa1VhygYsCfZhZtLnuCfXJ7fwn3f76XS4s2NFEKILBNC2A83v/7y2/bH5cXjx48vDnOTjvOoZNkqmld4LuAvtyWU53cplc5zJSnjEUUsS5nJTEppra1sFY+MnjsQYEsoz+/SpVtVUimtlKRJQ8phMmOMMbLDr3OzAgJsCeX5feQexZT1kgSYVzrXOq+KRVkWZbGYWvxeaiDAllCe3y/cNqc1n2l/les8z6tyURRlAQHGgABbQi3eIye8XCqt87vBlzY617k2xaIoi6Kc/KCsLxBgSyjP7+duEFJLpZW6E6BSWmml1PvMlMYYCDACBNgSGvXmBztm/8bRfillllnbatrvbEkuwKnEBf7ZtXBr19ejeb4U31dWthailsIWH62a8WS7kkIut0/VerPZrPTxfN7jfCFgD1rAAEoqrfd2i86P+nj0H9//E3fv3/mATZZImDkQYICVe8ReONuF3qfIU9//I5RWSiltPojSGGMWU13TJRUQYICNG+WS30dCIwH6/h+RV/tR8Ls6K8siq6e6rl8qIMAAF57f9zf3Pvl6vv9H5FWe5zqvilrqQmd27nlAYkCAATZS5TpXB7+PBEgtoO//EdrkWufalHVWFloeWkAI8GEgwAAr1/J97g1C6IIpz/877HcLBRphSmPMggSI8e/DQIABVLY3nMnvo/epJZOe/3fYn5EP2G79v7my+GPPE1x581zPlV/co/aZG/VS/o5XFMdH+yPneV28e/Prm5sPh9+A3aP8Z/eyaRxg03nCMU59PUG0gI61E9jSCabreTKvZbTCVlnjbCDpOfXfAiFAx2MnvIvA6LYpqtJKKXXwn8tMLOSCTwenfoNPvX6j8aXr6z3K9oOOrlkwtdW5znVNx5eVFQtRswnw1MPBIEDHk499v+x1x/PkdZ7neX5Iq1BIK0Rt2VyYU19PEAJ0fOXslivXEnYVoK611jqv6XUpayFEzXb/T309QQjQQYbzRqpc92ivtFVKH/UBjRQLKdEHDHHq9RsNF3aV5ZnSfdRC+ULodSWstRXfWHTyo+CXLi/Gd2eSJ/i/3Wi3cIONP9z90lFtt3fl9v6mObRkzzOlc62yn9w2lN/ja7P7u5vbnflPs7u9ud2Z7zwfkPy9Z4H1A9v6f5fuulOXgfqyP7utv57gqfmCs2sBKb4vD8zrTUVm92ceywds+gmn1iLOToAU5bJ0g41QXF9fVK2UVmo0H7Bp3OGp3fBTq8/g0GDjsXsEh+L6+qIXtix0tRjLB2wad3hqvuDsBPiF65N947ahuL6+5LLK89IuxvIBm/4BnZovODsBPnKj3d+5R/E2ENfXl3xRlVpXi7F8wKYCPDVfcHYCvHCP3i/dIOR/A3F9fVG1Ukob8X4kHxB9wDNheWw470fBD8b19YXOO5YPONtR8LU1VbZS9spsbbXblafiLxH+en7/7N4/+G/evN6P5/EKQaH4r9zxsSnPbytTZWtd/eR8wNfuuCeeL0fxk5d0XLevJ/5W7m5v3m+NaLg+I30OxSX+m9uG8hsPzeRbQH89v1jfJ7TfCltVWXM/j3zAWLm+DqFbgMF2bdm4E5xMXoD+en6xvlJof5m168uRDxij77zh0hhjjCmX8aIPwj1XZfIC9Nfzi/llof17P6+5nUI+YKxc33nDpSmKsiw7C5B7ef/JC9Bfzy82Tze0v5B1Kz+PfMBYub7zhgtTlEVRdNbxhVePsZm8AP31/GLzdEP72/p55APGytGN77qeU1mWRVEWnZ/kn3n1GJvpC9Bbz+82Uj50J40Ui0XLPqDScV307QPue4Cm82lI+G961qMrkxegv55fzO4INXFt/bym/mLfUXDlFiLsejy3AHp//qmuB/gXvdpcbFbqewq1r0xZfLityH97TnFz7jX5fzQP+Af3+pk1RVEaS37eW7WS2XqlQvGAxJNy7wP+YMuyKE11JYSoHpBJW9+UfMNXrl5CiPX6yyNf8+i8D33evXrS1tWT4hHH8gW5/wAGY61X6/V6pagF9PdbYe2xrxe6V1JImVWytQ+4Xx9BSimyKuUPLVQDWnkhVZQD1XNsX3CyAnzsViileD9/v9+nC3WilBV1bYUlH7Bp3g8ljFJKC2WFtXXvJ+0BqqepjDGmTBZmo6ywdS3s2L7gZAX4lVptLjYrTaNgf38h6498vZCPkduFqIWsyAdsGk+nF1VZ6EpU1UIIsUgmQKpnYU1RFsZ29f98clfPsX3ByQrwa73abDZrRWu6+I/YIqtrUd/ZKiEbIq8WC7FYZOQDNhVgLm2Zl3ZRZXIhRLqI/8N8YzcXJNV580ouhFjYsX3ByQrwK7X+7GKz0m9cvJ8vQD9SOdQC6kyKWixsBx9Q67xaVK4F7fAVHuSw4sLdICQJ++8pq7F9wckK8JEbBd8E4v2MFHKxaNQHtHUtbBcfUCsllBW1rdNFQR36gLY0xphkwnZ9VTm2LzhZAR6Ngh/04/zZap8YBWeVlO19wH0YjqVRdCoOo+DEyw9KIbOskqMLon88oPPHKC4wRaW68O+uz/ePerW5+GytroQQNzfiEO93HOd3/Npfr++F1Drf3M0RIZ/w95Upi/LWPstUVm3L4OCT4ghf7+MBy60XF/htz++5dY/ey0AfsKn/50Pf84m3/clt/+Z8wtT+4GRawKVa5stlrpd6ucyXy+D3orao8rZD0TQusCljpx8hXzC1n0lMRoAXruW7cH2/UJ+OLqjxtkPRNC6wKeT/JTthBPIFU/uZxGQESIOOx270+0ugHAmw9LZD0TQusCnk/6U6XwzyBfPEfiYxGQF+4fqAj11LGBIg+XiFtx1q4m7TuMCmpPb/YpAvqBP7mcRkBPjICe+xWm0uNuH7TS0ghWUNncejaVxgU1L7fzEOLWBiP5OYjAA/d4/eC7XefOrnJPrCJLwx+oBN4gKbktr/i3HUB0zqZxKTEWCu8+VyuVRLtfzkr2XU0lETMvSdTD3veOz0IySQ1H6mf/7OXB2tf8cRF/g/LujgX6hvZN69e6jcCy/O75ri3ygVl78/sg5gjG/d+oJ03leV2V6XpX3r4hOfeOWvA+v5kd9H/p/IsqUQ9+MwnweOP+yn6+BtY3lL/uyuD9X32tVj+8mjmtNbgNx5MDYu7Kptft6hfK3w51kpZdZ5xdTU8X9d0ZWo6/6B3Ad6C5A7D8aXrgU01pjSGNt4jRT3G++glfv482pbC2msWMhF+zFP6vi/rqysyWSmqgcfMx3oL0DmPBi/c4OPsjJlWZa28Tp5A/laIbTdf17X1bJSx/91ZWNLrUtThWyutvQWIHcejKfO/ytcn66pr0fxb2MNJ3MrhRDSFtIKIdqvpDG2/xdik7ge/VtA5jwYv3MCvLGmLAtjm/p6FP82mgBdi3u7v16tBTi2/xdiY02p9QkJkDsPxpcUeGpNaVr2AW0t5G7Q2h19nhS1rUXVtc88tv8XYl2ZTGbpfovuLUDuPBgbvdpsNrlwi0S1GAXv49/GgkbdVshOwSynkn5YyUWdMiihc75gyhP8wstL0TYvblt+dj5a4X56o/i0Sxd3d+n5d09dn4V8r2sv728oL0eo/iF/8B+E2N3ebs3fu8HCv97Vdx8HSL6j2/9P3vGX4mGuXPlY/pBnER+R/Fry8/zzdb1fP7Ys739O8l9CpLdNjZJaa6WyhYv7i7Ul5A8OVJ0jalOWxty6vpr/gdQCLm22WCzuTzAOVZBa9r61G8qvbXuf/fLJBUgnHGqC88qt8SwicX8E+YMDVeeOervb7XblL67F8etFvuNFYBQZXJMmUd9vKL+2rYB8XSQXIP0QO5QAN26Nl9wNPn6NlCd/cKDqHNiZ3e3t7a68dY9M34+kUfBj13W5V8/AeQs3uu9bv6H82rZ5R0YT4FAJUSjTEUVAx9Kqkj84UHUO3Lq1YH7dP4Irf1oj+Y6PAwGloWmQZZXGdxvKr217n/3yyQWovW1qNk6AFPcX++UjVQsS47bc3d6+35q/OsH79SLf8bET1Hu/noHzFon8v6H82rb3efAWUHnb1Eg3iqW4vwZ9wNKM0Afcmt1ut92ZX5xg/JhE8h1z10L6Agz3AY1JUf+h/Nq299kvP9goeKjf5jK34CTF/TUYBScZRcYwpiyNMaUbBVd+ZBr5jnklF4sHknOGR8FpZsEN5de2PaOvi84CpDzBQq+ybLVW33c9UQDfF/sPF/Wydj7gpS3/j/yt4/wYLzyfj/L+hvqKfpxcW/5BCHH7y2/b/3J9u4f6RFVFcXWfZaIyKlPKCppn67aUj8T/3q/8k3n8RHmH3eAsVI780peeL/hz6ICG0Dxnmmf9MlLe35+sBUzdufAfSW61vbv8vgd/b7/OH2cGoOJDUVBL27Qe5Av6cYltm2q3AgNtGs8aSu0L0v1vO/kgmQBTz63wf6NVWqn9Sn/7fw/+nluzhTENafHu5t2W/Lqm4VI0z9afb9v2OtIa2JQPpemiQql9Qap320jpZAJMPb/W/9vUWus814rW+zv4e26dPz4B1m/e/PZmS6PtpgIkX9CPS2x7HV0eFEn5UBoLMLEvSPX2B1cxkgkw9XJe/iM913me57mi/L4Hf8+t88eWcMW++fXX337btvXraJ6tP9/Wv47RuMZD3uNmueIOn5PYF6R637Q87mRbQL8pz1WutdY6d+v9UfyfYI7I3m5/e/PmzZttW7+O5tn68219AcbmeWn3BxlaCTZEal+Q7n8sDYbPyQrQ7wOu1L4XqNx6fwd/z1vnb2zebW/e3by72bb162iOiD/ftnUnPlNKKS2pL9j0uNS+IN3/tvGVyQSY2mjzBf1ZdhgAy2w/Cnb+Hu+svKL4UHz48OFDW7+O5tn6823bfhPvsjQWVGpfkOrdtiH6fzJN22JxhVHjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=640x480>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calling** the Prorgam:\n",
        "\n",
        "    python videorecdctblocksidctquant.py\n",
        "\n",
        "**We see**: Instead of long contours in the lightness jumps we now have **blocking artifacts**, similar to low pass filtering."
      ],
      "metadata": {
        "id": "PWyXm5112q14"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Writing bits to files in Python**\n",
        "The smallest unit we can write to a file in Python is 1 byte (8 bits). Since we only have 2 bit per index in the example, we must first form a bitstream, which we then divide into bytes.  The following function '*data2codestring*' in  File *writereadbits.py* generates the bit stream by connecting **codewords**."
      ],
      "metadata": {
        "id": "Fn_jgtL_2yE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def data2codestring(data):\n",
        "   #usage: codestring=data2codestring( data)\n",
        "   #takes 2 bit values in array \"data\" and returns the string of codewords\n",
        "\n",
        "   #sequence of integers (indices):\n",
        "   #limit data to range of codebook (-2 to 1):\n",
        "   data=np.clip(data,-2,1)\n",
        "   codeword={0:'00',1:'01',-2:'10',-1:'11'}\n",
        "   #start with empty string:\n",
        "   codestring='';\n",
        "   for value in data:\n",
        "      #append new codewords:\n",
        "      codestring=codestring+codeword[value]\n",
        "   return codestring"
      ],
      "metadata": {
        "id": "u_UMMBx923Jl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "where \"value\" is our index to be coded.\n",
        "\n",
        "Converting the **code string** back into a **sequence of 8-bit numbers** is done with the command \"eval\", which executes a string. This is done in the following fuction 'writebinaryfile' of the file 'writereadbits.py':"
      ],
      "metadata": {
        "id": "V2P5cor126k9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def writebinaryfile(filename, bitstring):\n",
        "   #usage: writebitsfile('filename.bin', bitstring)\n",
        "   #writes bitstring into binary file 'filename.bin'\n",
        "\n",
        "   import numpy as np\n",
        "   import struct\n",
        "\n",
        "   #use unsigned Bytes ('B'),\n",
        "   #converts stream of bits into stream of Bytes (groups of 8) represented as decimal integers:\n",
        "   numbytes=len(bitstring)//8;\n",
        "   Bytes=np.zeros(numbytes,dtype=int)\n",
        "   for m in range(numbytes):\n",
        "      Bytes[m]=eval('0b'+bitstring[m*8:(m*8+8)])\n",
        "   #packs the Bytes into a stream:\n",
        "   s=struct.pack('B'*len(Bytes), *Bytes)\n",
        "   #Write the stream \"s\" to file:\n",
        "   file=open(filename, \"wb\")\n",
        "   #'w' deletes exisiting file, 'a' appends\n",
        "   file.write(s)\n",
        "   file.close()"
      ],
      "metadata": {
        "id": "AseVBY0V3F5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here partial strings of 8 characters each are evaluated, with the string' 0b' at the beginning they are interpreted as binary numbers, which are written into the array \"Bytes\".\n",
        "\n",
        "The conversion of **the array** of 8-bit numbers into a **stream of bytes** is done with the command"
      ],
      "metadata": {
        "id": "I-FCDcu13Lzz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import struct\n",
        "Bytes=[255] #Example\n",
        "s=struct.pack('B'*len(Bytes), *Bytes)\n",
        "print(\"s=\",s)"
      ],
      "metadata": {
        "id": "pbkqSFOC3Rtb",
        "outputId": "39a70cd5-f1c1-49dc-eb0a-f60ec542a1cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "s= b'\\xff'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "'B' means: unsigned byte, because we already have the sign in our code words. bytes means: Pointer to our array with 8-bit numbers, i. e. integers between 0 and 255.\n",
        "\n",
        "###**Example:**"
      ],
      "metadata": {
        "id": "07ePS3IY3Lrt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import struct\n",
        "import numpy as np\n",
        "bitstring='000000010000001011111111'\n",
        "#Generate our Arrays of 8-bit numbers:\n",
        "Bytes=np.zeros(3,dtype=int)\n",
        "for m in range(3):\n",
        "\t   Bytes[m]=eval('0b'+bitstring[m*8:(m*8+8)])\n",
        "print(\"Bytes=\", Bytes)\n",
        "#array([  1,   2, 255])\n",
        "#Erzeugen des Byte-Stroms:\n",
        "s=struct.pack('B'*len(Bytes), *Bytes)\n",
        "print(\"s=\", s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWS5Ijqc3bLJ",
        "outputId": "49e846ef-734e-420e-e610-9261f5b25e83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bytes= [  1   2 255]\n",
            "s= b'\\x01\\x02\\xff'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "'\\x..' means a \"hexadecimal\" number whose digits consist of 4 bits. 4 bits can accept 16 values, e. g. between 0 and 15.10 to 15 values are represented by the letters 'a' to 'f'. Hence the hexadecimal number 'ff' for 255, which consists of two binary numbers '1111', ie hexadecimal 'f'.\n",
        "\n",
        "We can then write this stream to a file, with"
      ],
      "metadata": {
        "id": "kon-9SSa3m46"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filename='savebin.bin'\n",
        "file=open(filename, \"wb\")\n",
        "#'w' deletes exisiting file, 'a' appends\n",
        "file.write(s)\n",
        "file.close()"
      ],
      "metadata": {
        "id": "jbJyH5673vEy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We implement this as a function '*writebinaryfile*' in the file '*writereadbits.py*'.\n",
        "\n",
        "#Decoder\n",
        "In the **decoder** we have to reopen this file and read the byte stream, as in this function:"
      ],
      "metadata": {
        "id": "JxeNPlZ533Wi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def readbinaryfile(filename):\n",
        "   #usage: bitstring=readbinaryfile('filename.bin')\n",
        "   #reads binary data from file 'filename.bin' and converts it into a string of bits\n",
        "\n",
        "   import struct\n",
        "   import numpy as np\n",
        "\n",
        "   file=open(filename, \"rb\")\n",
        "   #read the stream from file:\n",
        "   readdata=file.read()\n",
        "   #unpacks the stream into an array of Bytes (8 bits):\n",
        "   Bytesread=struct.unpack('B'*len(readdata),readdata)\n",
        "   #Afterwards we create a bitstream from it,\n",
        "   #by creating bits from the bytes and connecting them to each other:\n",
        "   #Convert bytes into binary string:\n",
        "   bitstring='';\n",
        "   for byte in Bytesread:\n",
        "      #create bit strin from byte:\n",
        "      bits=bin(byte)\n",
        "      #remove leading '0b' and fill up to 8 bits with leading zeros:\n",
        "      bits=bits[2:].zfill(8)\n",
        "      #append to bits to bitstring:\n",
        "      bitstring=bitstring+bits\n",
        "\n",
        "   return bitstring"
      ],
      "metadata": {
        "id": "pXkb3OME3_NG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our data values (the codestring) are then translated from the pairs of 2 in the bit string, as in the follwong function:"
      ],
      "metadata": {
        "id": "eD2mr6MF4Vzt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def codestring2data(codestring):\n",
        "   #Decoder:--------------------------------------\n",
        "   #usage: data=codestring2data(codestring)\n",
        "   #reads binary data from codestring and converts it into a stream of 2 bit\n",
        "   #values returned in data.\n",
        "   import struct\n",
        "   import numpy as np\n",
        "\n",
        "   #Bits decoder, decodes the indices from the binary codewords:\n",
        "   decodeword={'00':0,'01':1,'10':-2,'11':-1}\n",
        "   #convert sequence of Bytes into sequence of bits:\n",
        "   numdata=len(codestring)//2;\n",
        "   #converts groups of 2 bits into data:\n",
        "   data=np.zeros(numdata,dtype=int)\n",
        "   n=0;\n",
        "   for i in range(numdata):\n",
        "      #print \"i= \", i\n",
        "      data[n]=decodeword[codestring[(i*2):(2+i*2)]]\n",
        "      #print \"data[n]= \", data[n]\n",
        "      n+=1\n",
        "\n",
        "   return data\n"
      ],
      "metadata": {
        "id": "vrAGZ9Gi4XYO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example"
      ],
      "metadata": {
        "id": "uimr4GugQNem"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#emulation of an encoder, example data:\n",
        "data=np.array([-2,-1,0,1,-2,-1,0,1])\n",
        "print(\"data encoder= \", data)\n",
        "filename='savebin.bin'\n",
        "codestring=data2codestring(data)\n",
        "print(\"codestring encoder= \", codestring)\n",
        "#write to binary file\n",
        "writebinaryfile(filename, codestring)\n",
        "\n",
        "#Emulation of a decoder:\n",
        "codestring=readbinaryfile(filename)\n",
        "print(\"codestring decoder=\", codestring)\n",
        "dataread=codestring2data(codestring)\n",
        "print(\"data decoder= \", dataread)"
      ],
      "metadata": {
        "id": "bYezwhJUQRlV",
        "outputId": "4968f237-0b05-41fd-a1aa-a06eb76f5767",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data encoder=  [-2 -1  0  1 -2 -1  0  1]\n",
            "codestring encoder=  1011000110110001\n",
            "codestring decoder= 1011000110110001\n",
            "data decoder=  [-2 -1  0  1 -2 -1  0  1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following functions serve as an **example** for translating a data array from our quantization indexes into strings from code words and back, *data2codestring* and *codestring2data*, in the file *writereadbits.py*. The code string can be defined using the functions *writebinaryfile* and *readbinaryfile* written to a binary file or read from it.\n",
        "\n",
        "We test these functions with:\n",
        "\n",
        "    python writereadbits.py"
      ],
      "metadata": {
        "id": "mpg4k9ES4fvY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**: The test routine is only activated when you call the function directly under “*if __name__ == '__main__':*” not when importing the file.\n",
        "\n",
        "**Note**: The test function shows: The functions *data2codestring* and *writebinaryfile* write a sequence of indexes with 2 bit each into the file '*savebin.bin*', and *readbinaryfile* and *codestring2data* decode the same sequence again."
      ],
      "metadata": {
        "id": "fkoWdgkX4vLU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Example of a frame encoder and decoder in Python:**\n",
        "The following **encoder** takes the lowest 3 DCT coefficients (at the positions (0,0), (0,1), and (1,0), quantizes them with 2 bit each, and stores them in their own file.  For simplicity's sake, we define the 8x8 pixel blockwise DCT and inverse DCT as function '*dct8x8*' or '*invdct8x8*' in the file '*blockdct.py*'. The size of the frame is additionally written in a text file for the decoder.\n",
        "\n",
        "We start the encoder with\n",
        "\n",
        "    python frameencfile.py\n",
        "\n",
        "We see the live video and save a frame when we press \"*c\" (capture*), in the files *y10enc. bin, y01enc. bin, y00enc. bin, framedim. txt*.\n",
        "\n",
        "If we look at the total size of the files, we get only **3.6 kB**, an **extreme compression** for a 480x640 pixel image!"
      ],
      "metadata": {
        "id": "Qxj-tacX5CD9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The corresponding **decoder** is started with:\n",
        "\n",
        "    python framedecfile.py\n",
        "\n",
        "This automatically reads the encoder's files, decodes the image and displays it.\n",
        "\n",
        "**As we can see**, it is very blocky and has only a few shades of gray, a consequence of extreme compression. But we can still see the content of the picture well."
      ],
      "metadata": {
        "id": "By6Osryu5iKU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**The Color Image Coder**\n",
        "By including the color components U and V (or Cb and Cr), our coder can be expanded to a color image encoder. Since the eye is sensitive to the color components at lower spatial frequencies than to the brightness in the luminance component Y, we limit ourselves to coding the DC coefficients (position (0,0)) of the block wise DCT of the color components U and V.\n",
        "\n",
        "The call of the encoder is most appropriate in a **subdirectory** for the resulting compressed image files:\n",
        "\n",
        "    mkdir Pic1; cd Pic1\n",
        "\n",
        "    python ../picturecolorencoder.py\n",
        "\n",
        "The decoder is then started with\n",
        "\n",
        "    python ../picturecolordecoder.p\n",
        "\n",
        "**Note**: The resulting files of the compressed color image have a total size of only about **6KB**, i. e. only about **0.16 bit/pixel** at 480x640 pixel image size!\n",
        "The resulting decoded image has clear artifacts (partly \"artistic\" in appearance), but the image content is still clearly visible."
      ],
      "metadata": {
        "id": "5PF9pJ3y5qCL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Python example for the different quantization of the DCT coefficients:**\n",
        "In order to use the different sensitivity of the eye for different spacial frequencies in order to improve the result, we quantize the respective DCT coefficients differently. We use the **Contrast Sensitivity Function** here only qualitatively.\n",
        "\n",
        "Reminder: In slide set 4 we saw the Contrast Sensitivity Function (CSF):"
      ],
      "metadata": {
        "id": "vZr-LwfH6DBZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Do2xoYf26IO-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We assume that the spatial frequency range of the DC coefficient of the DCT is already in the maximum of the CSF (according to our example above, where we found about 1 period/degrees per DCT subband and fitting to the curve for dark light). We therefore use the most accurate quantization for the DC value,  so we use the characteristic of the eye that it becomes increasingly **insensitive** to **high spatial frequencies** (fine patterns). For the coefficients with a constant sum s=k+l (for k,l: horizontal and vertical DCT subband indices)  we select the same quantization level, because they correspond to roughly to **edges of the same slope** in different directions. This results in anti-diagonals with the same quantization step-size in our quantization matrix. At the top left is DC  with **the highest accuracy (low quantization step sizes)**, down to the right, towards **high spatial frequencies**, the accuracy becomes smaller and **smaller (higher quantization step sizes)**.\n",
        "\n",
        "This will give us a more detailed quantization mask. To make it in python we use the command *np.diag*, which creates a diagonal matrix with a given offset from the main diagonal:"
      ],
      "metadata": {
        "id": "IdjAFcZk6Mkd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "B=np.diag([1,2,3])\n",
        "B"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTVMGQap6pZ6",
        "outputId": "c8cdfc3c-81da-490e-9cc3-95fbfaeb67c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0],\n",
              "       [0, 2, 0],\n",
              "       [0, 0, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "to make it an anti-diagonal:"
      ],
      "metadata": {
        "id": "H2iA3x1j664a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.fliplr(B)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9kYl4xv6-IT",
        "outputId": "3580283a-968f-404a-bebb-dfdc3b76f153"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 1],\n",
              "       [0, 2, 0],\n",
              "       [3, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "and np.tril, which generates a lower triangle matrix:"
      ],
      "metadata": {
        "id": "7AJl2XX87Eeq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.tril(np.ones((4,4)),1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FFyYUvS7GKB",
        "outputId": "63ec3a48-1a26-4b70-fb57-2761cdb7948c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 0., 0.],\n",
              "       [1., 1., 1., 0.],\n",
              "       [1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is now our quantization matrix M:"
      ],
      "metadata": {
        "id": "TnjE_ezW7IiY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Steps for different spatial frequencies:\n",
        "bits=4\n",
        "quantstep1=5.0/(2**bits-1)\n",
        "bits=3\n",
        "quantstep2=1.0/(2**bits-1)\n",
        "bits=2\n",
        "quantstep3=0.6/(2**bits-1)\n",
        "bits=1\n",
        "quantstep4=0.4/(2**bits-1)\n",
        "bits=0\n",
        "quantstep5=8.0/(2**bits-0.99) #vermeide div. durch 0!\n",
        "#Zus.: 1*4 bits + 2* 3 bits + 3*2bits + 4*1 bits, 20bits fuer 64 pixel, also 0.3125 bit pro pixel!\n",
        "#Quantization steps in \"mask\", anti-diagonals have the same quantization steps:\n",
        "M=np.zeros((8,8))\n",
        "M[0,0]=quantstep1\n",
        "M=M+  np.fliplr(np.diag([1,1],6))*quantstep2\n",
        "M=M+  np.fliplr(np.diag([1,1,1],5))*quantstep3\n",
        "M=M+  np.fliplr(np.diag([1,1,1,1],4))*quantstep4\n",
        "M=M+  np.fliplr(np.tril(np.ones((8,8)),3))*quantstep5\n",
        "print(M[0:3,0:3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8pnsVbZ7KM8",
        "outputId": "b6133cf3-24d6-49d7-c55b-36601c503d29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3.33333333e-01 1.42857143e-01 2.00000000e-01]\n",
            " [1.42857143e-01 2.00000000e-01 4.00000000e-01]\n",
            " [2.00000000e-01 4.00000000e-01 8.00000000e+02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "We then apply this matrix to each 8x8 DCT block. Each DCT coefficient is divided by the corresponding value in the quantization matrix M. Note that the coefficient in the upper left corner corresponds to the DC coefficient, and thus to the lowest spatial frequency. To the right and down, the corresponding spatial frequencies are getting higher and higher, so we can quantize them more and more coarsely.\n",
        "\n",
        "We get\n",
        "* 4 bit for the DC coefficient,\n",
        "* 3 bit for the following anti-diagonal, which consists of 2 coefficients, then\n",
        "* 2 bit for the following 3 coefficients on the next antidiagonal,\n",
        "* 1 bit for the next 4 coefficients, and\n",
        "* 0 bit for the rest.\n",
        "In this way we need the following number of bits per 8x8 pixel block:\n",
        "1*4 bits + 2* 3 bits + 3*2 bits + 4*1 bits=20 bits for 64 coefficients or pixels, thus **only 0.3125 bits per pixel**!"
      ],
      "metadata": {
        "id": "eZBxlCii7O4A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For our frame, we only have to repeat this small 8x8 matrix-mask until it fills up the whole frame. We do this with the command np.kron. It implements the so-called Kronecker product.\n",
        "An example:\n",
        "\n",
        "\\begin{equation*}\n",
        "A =\n",
        "\\begin{bmatrix}\n",
        "1 & 1\\\\\n",
        "1 & 1\n",
        "\\end{bmatrix}\n",
        "\\end{equation*}\n",
        "and M is our matrix mask.\n",
        "\n",
        "Then apply:\n",
        "\n",
        "\\begin{equation*}\n",
        "np.kron(A,M) =\n",
        "\\begin{bmatrix}\n",
        "M & M \\\\\n",
        "M & M\n",
        "\\end{bmatrix}\n",
        "\\end{equation*}\n",
        "\n",
        "So we can easily repeat our mask up to frame size.\n",
        "In our example:"
      ],
      "metadata": {
        "id": "TYyqoZnv7WEB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r8=r//8\n",
        "c8=c//8\n",
        "Mframe=np.kron(np.ones((r8,c8)),M);"
      ],
      "metadata": {
        "id": "JUs2iFeW8H47"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then we **quantize** with the element-wise matrix division:"
      ],
      "metadata": {
        "id": "2H62bIpf7y0M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "index=np.round (X/ Mframe)"
      ],
      "metadata": {
        "id": "uZea8RJF8QAj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "and **de-quantize** with element-wise matrix multiplication:"
      ],
      "metadata": {
        "id": "MRNiMJoT8TMc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Xrek=indices*Mframe"
      ],
      "metadata": {
        "id": "Vyrw1DpZ8WH6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calling** the Python program:\n",
        "\n",
        "    python videorecdctblocksidctquantmask.py"
      ],
      "metadata": {
        "id": "N0aCVC7_8YtU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**: Despite the significantly lower bit rate of 0.3125 bits/pixel we have a significantly better quality than the examples with the same quantization height at 2 bits/pixel! But observe that we neglected the range clipping from using a fixed number of bits in this example.\n",
        "Since we have used the Contrast Sensitivity Function here only qualitatively and very roughly, we can expect **clear improvements** with more **precise application of the CSF**!"
      ],
      "metadata": {
        "id": "rtC-DPJT8fmH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Example JPEG compression:**\n",
        "JPEG stands for \"Joint Photographic Expert Group\", which has released the JPEG standard for image compression and encoding. This format is known from digital cameras, for example. It is also based on splitting the image into 8x8 pixel blocks, then a 2D DCT of these blocks, and then a quantization of the DCT coefficients. Our quantization mask M is called \"Q\" here. For a \"Quality\" setting of 50%, JPEG uses the following quantization mask:"
      ],
      "metadata": {
        "id": "golt73l08mFQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "A89zJy0M8pj4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**: Here we also see the smallest quantization-step-size at the DCT-coefficients of the small spatial frequencies, top left, with the highest accuracy. To the lower right, towards the coefficients of the higher spatial frequencies, the quantization step sizes become larger and larger, i. e. the quantization becomes inaccurate.\n",
        "\n",
        "Note also that the number range for the quantization steps is different here, because an un-normalized image is assumed (pixels in the range 0 to 255), unlike in our example, where the image has been normalized, to pixel values in the range 0 to 1.\n",
        "(See also: http://en.wikipedia.org/wiki/JPEG)"
      ],
      "metadata": {
        "id": "ZKHYGP3d8sNG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Python example with JPEG quantization matrix:**\n",
        "\n",
        "\n",
        "    python videorecdctblocksidctquantjpgmask.py\n",
        "\n",
        "**Note**: The quality of the reconstructed video is not much better (but somewhat, in the finer details of the picture, by including more higher spatial frequencies), but now at a bit-rate that is not exactly known. The number of bits in JPEG is variable and depends on the image, because it uses Huffman coding, with variable lengths of codewords."
      ],
      "metadata": {
        "id": "A0mjMPx_8xdJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Conversion into a sequence of indices or bits**\n",
        "In JPEG, after quantization of the DCT coefficients and conversion into bits, the so-called \"zigzag scan\" follows. The following figure shows an 8x8 block of DCT coefficients, with the DC coefficient at the top left:"
      ],
      "metadata": {
        "id": "zXeS9h1185UW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "0WbHamTb89Ve"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thus, the coefficients are sorted by importance or bit length, and the transmission can be stoped in one place, depending on the \"Quality\" level (see Reimers:\"DVB-Digital TV technology\")."
      ],
      "metadata": {
        "id": "hmGngRIF8__W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**JPEG Processing steps:**\n",
        "##**Encoder:**\n",
        "- Y Cr Cb Color transform\n",
        "- Subsampling of Cr, Cb (4:2:0)\n",
        "- Apply 2D-DCT to 8x8 pixel blocks on the 3 components\n",
        "-Quantization using the quantization matrix\n",
        "-Conversion of the indices from quantization to bits.\n",
        "\n",
        "##**Decoder:**\n",
        "- Conversion of the bits into indices\n",
        "- De-quantization of indices into reconstructed values\n",
        "- Inverse 2D-DCT on the 8x8 blocks of the 3 components\n",
        "- Upsampling of Cr, Cb and conversion back to RGB.\n",
        "(See also: U. Reimers:\"DVB-Digital television technology\", Springer)\n",
        "\n",
        "##**Tips for debugging**\n",
        "- Divide a program into small functional units (e. g. functions), which can be tested individually for themselves\n",
        "-Use \"print\" commands, starting from the front (where it should be correct) and back (where it migth be buggy) to the middle of a program, to narrow down in which area an error (bug) has to occur until it is found.\n"
      ],
      "metadata": {
        "id": "fnJnkXXC9DVH"
      }
    }
  ]
}