Rate-distortion analysis for vector quantization based on a variable block-size classification model

Michael H. Lee; King N. Ngan; Gregory A. Crebbin

doi:10.1117/12.263295

10 January 1997 Rate-distortion analysis for vector quantization based on a variable block-size classification model

Michael H. Lee, King N. Ngan, Gregory A. Crebbin

Proceedings Volume 3024, Visual Communications and Image Processing '97; (1997) https://doi.org/10.1117/12.263295
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

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 proven that the R(D) bound of the VBSC model is lower than those of the Gaussian model and the FBSC model. We also experimentally evaluate a RDF for the VBSC model and compare with the theoretical RDF. There is a gap of about 0.1 bpp between the theoretical RDF and the experimental RDF in VBSC model-based VQ coding. We expect that this gap can be reduced by subsequently employing an entropy coder.

Citation Download Citation

Michael H. Lee, King N. Ngan, and Gregory A. Crebbin "Rate-distortion analysis for vector quantization based on a variable block-size classification model", Proc. SPIE 3024, Visual Communications and Image Processing '97, (10 January 1997); https://doi.org/10.1117/12.263295

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