Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/54765
Title: A Rate-Distortion Function for Vector Quantization with a Variable Block-Size Classification Model
Authors: Lee, M.H.
Ngan, K.N. 
Crebbin, G.
Issue Date: Dec-1997
Source: Lee, M.H.,Ngan, K.N.,Crebbin, G. (1997-12). A Rate-Distortion Function for Vector Quantization with a Variable Block-Size Classification Model. Journal of Visual Communication and Image Representation 8 (4) : 356-363. ScholarBank@NUS Repository.
Abstract: In this paper, a rate-distortion function (RDF), R(D), is presented for a variable block-size classification (VBSC) model. We obtain a theoretical R(D) bound on the performance of vector quantization (VQ) based on the VBSC model. It is theoretically proved that the R(D) bound of the VBSC model is lower than those of the Gaussian model and the fixed blocksize classification (FBSC) model for the bit rates of interest. In the comparison tests of VBSC model-based VQ and FBSC model-based VQ, which were carried out by using a monochrome still image, it was seen that the former technique outperforms the latter technique, subjectively as well as objectively. We also experimentally evaluate a RDF for the VBSC model and compare this with the theoretical RDF. There is a gap of 0.07-0.1 bpp between the theoretical RDF and the experimental RDF in VQ coding without entropy coding. We have reduced the gap to 0.02-0.03 bpp by subsequently employing a Huffman coder for entropy coding. It is expected that the theoretical bound can be approached more closely by the experimental RDF by using a modified asymptotic RDF. © 1997 Academic Press.
Source Title: Journal of Visual Communication and Image Representation
URI: http://scholarbank.nus.edu.sg/handle/10635/54765
ISSN: 10473203
Appears in Collections:Staff Publications

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