This project inspired by Google's paper Full Resolution Image Compression with Recurrent Neural Networks (arxiv) and its TensorFlow implementation.
The code inside aims to compare (quantitatively and qualitatively) different aspects of compression done by this method and codecs popular today, in different compression levels, for different image resolutions.
Hardware:
- GPU is not necessary but preferable
- At least 3Gb of RAM
Software:
- Ubuntu 14.04+ (tested)
- TensorFlow
- Pillow 3.4+
- NumPy 1.11+
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First, install suitable TensorFlow version. See instruction here.
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Install other project dependencies:
./install_deps.sh
First, download Google's pre-trained model via download_model.sh. It required for all further actions.
If you want just to try Google's model, you should run python nn_compression_example.py with following parameters:
--image=/path/to/image- path to image;--quality=0|...|15- level of compression, default is8;--model=/path/to/model- pre-trained model file, default isgoogle's-compression-model/residual_gru.pb.
Notice that the image should have width and height multiple of 32 according the paper.
If you want to (re)run benchmarking, you should first prepare the set of test images by executing generate_test_samples.sh.
Otherwise, you can successively run python generate_test_samples_regular.py and python generate_test_samples_nn.py. First one will generate some test samples, comressed with the regular codecs (list of codecs you can find in the code) and also reshapes images to be multiple of 32 by both sides. Second script will generate a bunch of test samples, comressed using the method encoding. You can specify the following parameters for the last one:
--image_tmpl=root/to/images-regextemplate to images to be compressed, default istest_images/*_orig.ppm;--quality=0|...|15,...,0|...|15- levels of compression, default is3,6,9,12;--model=/path/to/model- pre-trained model file, default isgoogle's-compression-model/residual_gru.pb.
It required once if you don't want to change comression parameters or images set.
Finally, run python benchmark_quality_msssim.py for quality benchmarking based on MS-SSIM metric. It will generate results in CSV format.
Bellow you can see the quality benchmarking result based on MS-SSIM metric for a bunch of images. Clearly seen , that the quality of method's compression depends on image resolution.
All files in google subfolder have a license from the original project and was taken from tensorflow/models@76739168f61dd9bb849e500bbd235fa9e4b7612f .
License for the google's compression model (google's-compression-model) described in its LICENSE file.
Images for this project taken from the publicly available datasets, precisely
All files, that are the heritage of the project, subjects to WTFPL (see LICENSE file in the root).