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Bachelor Thesis utilizing an artifical neural networks and discrete wavelet transform in order to achieve state-of-the-art lossy image compression

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Image compression using artificial neural networks and discrete wavelet transform

Bachelor Thesis utilizing an artifical neural networks and discrete wavelet transform in order to implement a lossy image compression codec. Project based on paper "Variational image compression with a scale hyperprior"

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   ├── interim        <- Intermediate data that has been transformed.
│   └── raw            <- The original, immutable data dump.
│
├── external           <- Python dependencies that cannot be installed via package manager
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│   └── models.csv     <- File that translates training session id to the model params
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   └── models         <- Scripts to train models and then use trained models to make benchmarks
│       │
│       ├── networks   <- module with different neural network architectures
│       ├── gym        <- module with neural network training suite
│       ├── benchmarks <- module with neural network benchmarking suite
│       ├── benchmark_model.py
│       ├── train_model.py
│       └── update_config.py
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├── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io
│
└── config.ini         <- configuration file with the current network params

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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Bachelor Thesis utilizing an artifical neural networks and discrete wavelet transform in order to achieve state-of-the-art lossy image compression

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