  Recent advances in deep learning have led to superhuman performance across a
variety of applications. Recently, these methods have been successfully
employed to improve the rate-distortion performance in the task of image
compression. However, current methods either use additional post-processing
blocks on the decoder end to improve compression or propose an end-to-end
compression scheme based on heuristics. For the majority of these, the trained
deep neural networks (DNNs) are not compatible with standard encoders and would
be difficult to deply on personal computers and cellphones. In light of this,
we propose a system that learns to improve the encoding performance by
enhancing its internal neural representations on both the encoder and decoder
ends, an approach we call Neural JPEG. We propose frequency domain pre-editing
and post-editing methods to optimize the distribution of the DCT coefficients
at both encoder and decoder ends in order to improve the standard compression
(JPEG) method. Moreover, we design and integrate a scheme for jointly learning
quantization tables within this hybrid neural compression framework.Experiments
demonstrate that our approach successfully improves the rate-distortion
performance over JPEG across various quality metrics, such as PSNR and MS-SSIM,
and generates visually appealing images with better color retention quality.
