DocumentCode
2629298
Title
Classified vector quantization of images using texture analysis
Author
Li, Lihua ; He, Zhenya
Author_Institution
Dept. of Radio Eng., Southeast Univ., Nanjing, China
fYear
1990
fDate
1-3 May 1990
Firstpage
1007
Abstract
A new VQ-based image coding method, classified vector quantization using texture analysis (CVQ-TA) is proposed. The most notable differences between CVQ-TA and CVQ are: (1) the classification in CVQ-TA operates in the transform domain by means of texture analysis, and (2) product-code vector quantization is used to reduce the complexity increasing with the subblock size. Because of these characteristics, CVQ-TA can be applied for large block size (8×8 or 16×16), which is more efficient for block coding. Simulation results show that CVQ-TA can give a good visual perceptual quality
Keywords
data compression; encoding; video signals; CVQ-TA; block coding; classified vector quantization using texture analysis; good visual perceptual quality; image coding method; large block size; low bit rate systems; product-code vector quantization; transform domain; vector quantization of images; video encoding; Bit rate; Block codes; Discrete cosine transforms; Helium; Image analysis; Image coding; Image reconstruction; Image texture analysis; Performance analysis; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location
New Orleans, LA
Type
conf
DOI
10.1109/ISCAS.1990.112276
Filename
112276
Link To Document