Conditional Entropy-Constrained Residual VQ with Application to Image Coding

Kossentini, Faouzi; Chung, Wilson C.; Smith, Mark J. T.

This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.

Document ID

19970019691

Acquisition Source

Headquarters

Document Type

Reprint (Version printed in journal)

External Source(s)

doi:10.1109/83.480766

Authors

Date Acquired

September 6, 2013

Publication Date

February 1, 1996

Publication Information

Publication: IEEE Transactions on Image Processing

Publisher: Institute of Electrical and Electronics Engineers

Volume: 5

Issue: 2

ISSN: 1057-7149

Subject Category

Report/Patent Number

Accession Number

97N21370

Funding Number(s)

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Public

Public Use Permitted.

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