Rate-Distortion Analysis for Vector Quantization Based on a Variable Block-Size Classification Model

Vector quantization (VQ) based on a fixed block-size classification (FBSC) model, which is known as classified VQ (CVQ), offers a useful solution for the edge degradation problem of conventional image VQ. In our previous work, we have developed a VQ technique based on a variable block-size classification (VBSC) model, in which an image is segmented into blocks of various size, and each segmented region is encoded at a different rate according to its level of detail. The low-detail regions of the image consist of variable size blocks and are encoded at very low bitrates with little perceptual degradation. High-detail regions, which are isolated into the smallest blocks, are classified into various edges of which each is separately encoded. In this paper, a rate-distortion function (RDF), R(D), is presented for a VBSC model. We obtain a theoretical R(D) bound on the performance of VQ based on a VBSC model. It is theoretically proved that the R(D) bound of the VBSC model is lower than th..