DocumentCode
315850
Title
Designing better entropy-constrained vector quantizers via clustering and integral projections
Author
Wang, Ting-Chi ; Huang, Hung-Ru
Author_Institution
Dept. of Inf. & Comput. Eng., Chung Yuan Christian Univ., Chung Li, Taiwan
Volume
2
fYear
1997
fDate
9-12 Jun 1997
Firstpage
1325
Abstract
This paper presents a novel algorithm, which combines both the merits of clustering and integral projections, to solve the entropy-constrained codebook design problem. The experimental results indicate that the proposed algorithm is very efficient and is capable of generating better codebooks than the ECVQ algorithm
Keywords
entropy codes; image coding; vector quantisation; VQ method; clustering; codebook design problem; entropy-constrained vector quantizers; image compression; integral projections; Algorithm design and analysis; Clustering algorithms; Cost function; Design engineering; Distortion measurement; Encoding; Image coding; Iterative algorithms; Partitioning algorithms; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on
Print_ISBN
0-7803-3583-X
Type
conf
DOI
10.1109/ISCAS.1997.622098
Filename
622098
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