Yang S B, Tseng L Y
Dept. of Appl. Math., Nat. Chung-Hsing Univ., Taichung.
IEEE Trans Image Process. 2001;10(5):677-85. doi: 10.1109/83.918561.
Although the side-match vector quantizer (SMVQ) reduces the bit rate, the image coding quality by SMVQ generally degenerates as the gray level transition across the boundaries of the neighboring blocks is increasing or decreasing. This study presents a smooth side-match method to select a state codebook according to the smoothness of the gray levels between neighboring blocks. This method achieves a higher PSNR and better visual perception than SMVQ does for the same bit rate. Moreover, to design codebooks, a genetic clustering algorithm that automatically finds the appropriate number of clusters 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-match method. Experimental results indicate that SSM-CVQ has a higher PSNR and a lower bit rate than other methods. Furthermore, the Lena image can be coded by SSM-CVQ with 0.172 bpp and 32.49 dB in PSNR.
尽管边匹配矢量量化器（SMVQ）降低了比特率，但随着相邻块边界处灰度级的增加或减少，通过SMVQ进行的图像编码质量通常会下降。本研究提出了一种平滑边匹配方法，根据相邻块之间灰度级的平滑程度来选择状态码本。对于相同的比特率，该方法比SMVQ具有更高的峰值信噪比（PSNR）和更好的视觉感知效果。此外，为了设计码本，还提出了一种能自动找到合适聚类数的遗传聚类算法。因此，所提出的平滑边匹配分类矢量量化器（SSM-CVQ）是三种技术的组合：分类矢量量化、可变块大小分割和平滑边匹配方法。实验结果表明，SSM-CVQ比其他方法具有更高的PSNR和更低的比特率。此外，Lena图像可以通过SSM-CVQ以0.172比特每像素（bpp）的比特率进行编码，PSNR为32.49分贝。
