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Cited by 423

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Classified Vector Quantization of Images

Ramamurthi, B., Gersho, A.·1986·IEEE Transactions on Communications

Abstract

Vector quantization (VQ) provides many attractive features for image coding with high compression ratios. However, initial studies of image coding with VQ have revealed several difficulties, most notably edge degradation and high computational complexity. We address these two problems and propose a new coding method, classified vector quantization (CVQ), which is based on a composite source model. Blocks with distinct perceptual features, such as edges, are generated from different subsources, i.e., belong to different classes. In CVQ, a classifier determines the class for each block, and the block is then coded with a vector quantizer designed specifically for that class. We obtain better perceptual quality with significantly lower complexity with CVQ when compared to ordinary VQ. We demonstrate with CVQ visual quality which is comparable to that produced by existing coders of similar complexity, for rates in the range 0.6-1.0 bits/pixel.

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Metadata

Authors
Ramamurthi, B., & Gersho, A.
Venue
IEEE Transactions on Communications
Published
1986
Volume
34
Issue
11
Pages
1105-1115
ISSN
0090-6778 (ISSN-L)
OpenAlex
W2152037928
Cited by
423
References
15
FWCI
3.44
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Classified Vector Quantization of Images. https://app.cochat.ai/papers/10.1109/tcom.1986.1096468
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Ramamurthi, B., & Gersho, A. (1986). Classified Vector Quantization of Images. IEEE Transactions on Communications. https://doi.org/10.1109/tcom.1986.1096468

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