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Proceedings Paper

Noncausal predictive image coding
Author(s): Peifang Zhou; Masoud R. K. Khansari; Alberto Leon-Garcia
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Paper Abstract

This paper presents an application of Markov random field theory to image coding. First we use Markov random fields to model the correlation in the image intensity fields. We then propose a noncausal predictive image coding scheme in which the estimation of present pixel is based on both past and future neighboring pixels. A sequential iterative decoding algorithm is extended from 1D to 2D to perfectly reconstruct the image from estimation residuals at the decoder. We also develop a fast whirlpool algorithm to speed up the decoding. Open-loop and closed-loop quantizer structures are implemented for noncausal prediction and performances are compared with conventional DPCM predictive coding.

Paper Details

Date Published: 22 October 1993
PDF: 12 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157995
Show Author Affiliations
Peifang Zhou, Univ. of Toronto (Canada)
Masoud R. K. Khansari, Univ. of Toronto (Canada)
Alberto Leon-Garcia, Univ. of Toronto (Canada)

Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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