  In recent years we have witnessed an increasing interest in applying Deep
Neural Networks (DNNs) to improve the rate-distortion performance in image
compression. However, the existing approaches either train a post-processing
DNN on the decoder side, or propose learning for image compression in an
end-to-end manner. This way, the trained DNNs are required in the decoder,
leading to the incompatibility to the standard image decoders (e.g., JPEG) in
personal computers and mobiles. Therefore, we propose learning to improve the
encoding performance with the standard decoder. In this paper, We work on JPEG
as an example. Specifically, a frequency-domain pre-editing method is proposed
to optimize the distribution of DCT coefficients, aiming at facilitating the
JPEG compression. Moreover, we propose learning the JPEG quantization table
jointly with the pre-editing network. Most importantly, we do not modify the
JPEG decoder and therefore our approach is applicable when viewing images with
the widely used standard JPEG decoder. The experiments validate that our
approach successfully improves the rate-distortion performance of JPEG in terms
of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this
translates to better overall color retention especially when strong compression
is applied. The codes are available at
https://github.com/YannickStruempler/LearnedJPEG.
