Smooth side-match classified vector quantizer with variable block size

Although the side-match vector quantizer (SMVQ) reduces the bit rate, the i mage coding quality by SMVQ generally degenerates as the gray level transit ion across the boundaries of the neighboring blocks is increasing or decrea sing. This study presents a smooth side-match method to select a state code book according to the smoothness of the gray levels between neighboring blo cks. This method achieves a higher PSNR and better visual perception than S MVQ does for the same bit rate. Moreover, to design codebooks, a genetic cl ustering algorithm that automatically finds the appropriate number of clust ers is proposed. The proposed smooth side-match classified vector quantizer (SSM-CVQ) is thus a combination of three techniques: the classified vector quantization, the variable block size segmentation and the smooth side-mat ch method. Experiment al results indicate that SSM-CVQ has a higher PSNR an d a lower bit rate than other methods. Furthermore, the Lena image can be c oded by SSM-CVQ with 0.172 bpp and 32.49 dB in PSNR.