Lossless JPEG Recompression for Similar Images via Frequency Domain Block Matching

JPEG, a landmark lossy image compression standard, has been widely used over the past years, yet its simple design disadvantages the compression performance compared to other advanced algorithms currently available. This has led to the development of lossless JPEG recompression techniques to further exploit the frequency domain redundancy by reconstructing the binary streams without loss. However, existing work has not considered the lossless recompression problem of similar images, as it can utilize the mutual correlation between transformed coefficients to squeeze additional bits saving. As a result, this paper proposes a lossless recompression method for similar images through the block matching in frequency domain. Specifically, we fulfill the purpose by firstly applying the Controllable Similar Block Search (CSBS) to find the corresponding similar blocks in the transform domain. Built upon the similarity assessment, we deploy both the well-designed Optimal Motion Vector Coding (OMVC) and Variable-Length Residual Coding (VLRC) to improve the recompression efficiency. Experimental results demonstrate the superior performance of our method when compared to other popular JPEG recompression algorithms. To the best of our knowledge, this is the first lossless recompression work for similar images in the frequency domain.