Dithered soft decision quantization for baseline JPEG encoding and its joint optimization with huffman coding and quantization table selection

Abstract

Based on baseline JPEG, a new image coding framework is first developed, where dithered (uniform) quantizers are used to replace JPEG uniform quantizers for the purpose of improving the rate-distortion performance without sacrificing the coding complexity instead of the conventional subjective image quality. By combining dithering with soft decision quantization (SDQ)-yielding dithered SDQ, an iterative algorithm is then proposed for jointly designing dithers for DCT coefficients at each frequency (i.e., dither table), quantization table, run-length coding, and Huffman coding. The algorithm converges in the sense that its rate-distortion cost is monotonically decreasing until a stationary point is reached. When compared with state-of-the-art baseline JPEG R-D optimizer proposed recently by Yang and Wang, our algorithm achieves comparable and sometimes better rate-distortion performance with 65% computational complexity reduction.