Compressing Sign Information in DCT-based Image Coding via Deep Sign Retrieval

The discrete cosine transformation (DCT) [1] is known as an important technique for image coding and is adopted in various image coding standards [2, 3, 4, 5, 6, 7, 8, 9]. For instance, JPEG [2] first divides an original image into non-overlapping blocks and then applies DCT to each of the blocks followed by quantization. Entropy coding is finally performed to obtain bit representations for the quantized DCT coefficients. According to the source coding theory [10], statistically biased symbols can be efficiently compressed using entropy coding methods such as [11, 12, 13, 14]. However, the sign information of DCT coefficients has equiprobable characteristics [15, 16, 17], i.e., the probabilities of the positive and negative signs are almost even, and the compression of the sign information has been thus considered impossible. Therefore, each of the signs is represented using 1 bit in typical image coding methods; the sign information consumes many bits in the resulting bitstream. To reduce the bit amount for the signs, we address a sign compression problem for DCT coefficients in this paper. In particular, we consider a lossless sign compression problem, where the signs of the DCT coefficients are decoded without loss. We briefly summarize seminal works developed to tackle this challenging problem.