Paper Info
Title
Adaptive edge-based side-match finite-state classified vector quantization with quadtree map
Abstract
Vector quantization (VQ) is an effective image coding technique at low bit rate. The side-match finite-state vector quantizer (SMVQ) exploits the correlations between neighboring blocks (vectors) to avoid large gray level transition across block boundaries. A new adaptive edge-based side-match finite-state classified vector quantizer (classified FSVQ) with a quadtree map has been proposed. In classified FSVQ, blocks are arranged into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into 16 classes. Each class uses a master codebook that is different from the codebooks of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ and VQ coding techniques. As is shown, the improvement over ordinary SMVQ is up to 1.16 dB at nearly the same bit rate, moreover, the improvement over ordinary VQ can be up to 2.08 dB at the same bit rate for the image, Lena. Further, block boundaries and edge degradation are less visible because of the edge-vector classification. Hence, the perceptual image quality of classified FSVQ is better than that of ordinary SMVQ
Year
DOI
Venue
1996
10.1109/83.480774
IEEE Transactions on Image Processing
Keywords
DocType
Volume
edge detection,adaptive signal processing,vector quantization,psnr,degradation,image classification,clustering algorithms,image quality
Journal
5
Issue
ISSN
Citations 
2
1057-7149
13
PageRank 
References 
Authors
1.06
7
2
Name
Order
Citations
PageRank
Ruey-Feng Chang139534.88
Wei-Ming Chen210712.96