Abstract
Several low bit rate still image compression methods have been presented in recent years, such as SPHIT, hybrid VQ, and the Wu-Chen method. In particular, the image “Lena” can be compressed using less than 0.15 bpp at 31.4 dB or higher. These methods exercise the analysis techniques (wavelet or subband) before distributing the bit rate to each piece of image, thus the tradeoff between bit rate and distortion can be solved. In this dissertation, we propose a simple but comparable method that adopts the technique of side match VQ only. The side match vector quantization (SMVQ) is an effective VQ coding scheme at low bit rate. The conventional side match (two-sided) VQ utilizes the codeword information of two neighboring blocks to predict the state codebook of an input vector. In this dissertation, we propose a hierarchical three-sided side match finite-state vector quantization (HTSMVQ) method that can (1) make the state codebook size as small as possible; the size is reduced to 1 if the prediction can perform perfectly, (2) improve the prediction quality for edge blocks, and (3) regularly refresh the codewords to alleviate the error propagation of side match. In the simulation results, the image “Lena” can be coded with PSNR 34.682 dB at 0.25 bpp. It is better than SPIHT, EZW, FSSQ and hybrid VQ with 34.1, 33.17, 33.1, and 33.7 dB, respectively. At the bit rate lower than 0.15 bpp, only the enhanced version of EZW performs better than our method about 0.14 dB. Furthermore, we also apply the HTSMVQ to three practical applications, namely, (1) three-sided side match VQ for image sequence coding, (2) improving VQ coding quality by sub-codeword information, and (3) data loss recovery in vector quantization. Finally, this dissertation also presents a structured design of codebook to speed the encoding.