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    Neural Image Compression

    Quote Originally Posted by comp.compression
    I'd like to share a side project I worked on which generalizes transform coding to the nonlinear case. Here the transforms are represented by neural networks, which learn the appropriate form of the transform. The result of the transform is then quantized using standard entropy coding.

    Github: https://github.com/Justin-Tan/high-f...ve-compression
    Interactive Demo: https://colab.research.google.com/gi...lab_demo.ipynb

    There are some obvious shortcomings to this method - such as, that it only caters for image data, cannot be adjusted to attain a variable bitrate, short of training a different model, and is unrealistically slow for practical applications.

    I'm not a traditional compression expert, so would appreciate any insight about the deficiencies of this method from those who are. Note this is not my original idea and is a reimplementation.

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