Joint thresholding and quantizer selection for block-transform-based image coding with applications to baseline JPEG
Matthew S. Crouse, Kannan Ramchandran · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
We introduce a novel image-adaptive encoding scheme for the baseline JPEG standard that maximizes the decoded image quality without compromising compatibility with current JPEG decoders. Our algorithm jointly optimizes quantizer selection, coefficient 'thresholding', and entropy coding within a rate-distortion (R-D) framework. It unifies two previous approaches to image-adaptive JPEG encoding: R-D optimized quantizer selection by Wu and Gersho, and R-D optimal coefficient thresholding by Ramchandran and Vetterli. By formulating an algorithm which optimizes these two operations jointly, we have obtained performance that is the best in the reported literature for JPEG-compatible coding. In fact the performance of this JPEG coder is comparable to that of more complex 'state-of-the-art' image coding schemes: e.g., for the benchmark 512 by 512 'Lenna' image at a coding rate of 1 bit per pixel, our algorithm achieves a peak signal to noise ratio of 39.6 dB, which represents a gain of 1.7 dB over JPEG using the example Q- matrix with a customized Huffman entropy coder, and even slightly exceeds the published performance of Shapiro's celebrated embedded zerotree wavelet coding scheme. Furthermore, with the choice of appropriate visually-based error metrics, noticeable subjective improvement has been achieved as well. The reason for our algorithm's superior performance can be attributed to its conceptual equivalence to the application of entropy-constrained vector quantization design principles to a JPEG-compatible framework. Furthermore, our algorithm may be applied to other systems that use run-length encoding, including intra- frame MPEG and subband or wavelet coding.