Noncausal predictive image coding

Peifang Zhou; Masoud R. K. Khansari; Alberto Leon-Garcia

doi:10.1117/12.157995

22 October 1993 Noncausal predictive image coding

Peifang Zhou, Masoud R. K. Khansari, Alberto Leon-Garcia

Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.157995
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States

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.

Citation Download Citation

Peifang Zhou, Masoud R. K. Khansari, and Alberto Leon-Garcia "Noncausal predictive image coding", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.157995

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