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Proceedings Paper

Context-based entropy coding of block transform coefficients for image compression
Author(s): Chengjie Tu; Trac D. Tran
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Paper Abstract

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. Wavelet-based JPEG2000 is emerging as the new high-performance international standard for still image compression. An often asked question is: how much better are wavelets comparing to block transforms in image coding? A notable observation is that each block transform coefficient is highly correlated with its neighbors within the same block as well as its neighbors within the same subband. Current block transform coders such as JPEG suffer from poor context modeling and fail to take full advantage of inter-block correlation in both space and frequency sense. This paper presents a simple, fast and efficient adaptive block transform image coding algorithm based on a combination of pre-filtering, post-filtering, and high-order space-frequency context modeling of block transform coefficients. Despite the simplicity constraints, coding results show that the proposed codec achieves competitive R-D performance comparing to the best wavelet codecs.

Paper Details

Date Published: 7 December 2001
PDF: 13 pages
Proc. SPIE 4472, Applications of Digital Image Processing XXIV, (7 December 2001); doi: 10.1117/12.449796
Show Author Affiliations
Chengjie Tu, Johns Hopkins Univ. (United States)
Trac D. Tran, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 4472:
Applications of Digital Image Processing XXIV
Andrew G. Tescher, Editor(s)

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