Unified learning-based lossy and lossless JPEG recompression
Jianghui Zhang Yuanyuan Wang Lina Guo Jixiang Luo Tongda Xu Yan Wang Zhi Wang Hongwei Qin
JPEG is still the most widely used image compression algorithm. Most image
compression algorithms only consider uncompressed original image, while
ignoring a large number of already existing JPEG images. Recently, JPEG
recompression approaches have been proposed to further reduce the size of JPEG
files. However, those methods only consider JPEG lossless recompression, which
is just a special case of the rate-distortion theorem. In this paper, we
propose a unified lossly and lossless JPEG recompression framework, which
consists of learned quantization table and Markovian hierarchical variational
autoencoders. Experiments show that our method can achieve arbitrarily low
distortion when the bitrate is close to the upper bound, namely the bitrate of
the lossless compression model. To the best of our knowledge, this is the first
learned method that bridges the gap between lossy and lossless recompression of
JPEG images.