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As a commonly-used image compression format, JPEG has been broadly applied inthe transmission and storage of images. To further reduce the compression costwhile maintaining the quality of JPEG images, lossless transcoding technologyhas been proposed to recompress the compressed JPEG image in the DCT domain.Previous works, on the other hand, typically reduce the redundancy of DCTcoefficients and optimize the probability prediction of entropy coding in ahand-crafted manner that lacks generalization ability and flexibility. Totackle the above challenge, we propose the learned lossless JPEG transcodingframework via Joint Lossy and Residual Compression. Instead of directlyoptimizing the entropy estimation, we focus on the redundancy that exists inthe DCT coefficients. To the best of our knowledge, we are the first to utilizethe learned end-to-end lossy transform coding to reduce the redundancy of DCTcoefficients in a compact representational domain. We also introduce residualcompression for lossless transcoding, which adaptively learns the distributionof residual DCT coefficients before compressing them using context-basedentropy coding. Our proposed transcoding architecture shows significantsuperiority in the compression of JPEG images thanks to the collaboration oflearned lossy transform coding and residual entropy coding. Extensiveexperiments on multiple datasets have demonstrated that our proposed frameworkcan achieve about 21.49% bits saving in average based on JPEG compression,which outperforms the typical lossless transcoding framework JPEG-XL by 3.51%.

Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression