A switching predictor for lossless image coding

Lih Jen Kau*, Yuan-Pei Lin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

In this paper, we propose a switching adaptive predictor (SWAP) with automatic context modeling for lossless image coding. In the SWAP system, two predictors are used. For areas with edges, estimates of coding pixels are obtained using texture context matching (TCM). For all other areas, an adaptive neural predictor (ANP) is used. The SWAP encoder-switches between the two predictors ANP and TCM depending on the neighborhood of the coding pixel. The switching predictor allows statistical redundancy to be removed effectively. On the other hand, it is known that prediction can be further refined using error compensation. For this, we propose the use of a modified fuzzy clustering, which leads to a modeling of errors that adapts itself to the input statistics. Experiments show that the proposed context clustering is very useful in modeling error for prediction refinement. Comparisons of the proposed system to existing state-of-the-art predictive coders will be given to demonstrate its coding efficiency.

Original languageEnglish
Pages (from-to)228-233
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
DOIs
StatePublished - 24 Nov 2003
EventSystem Security and Assurance - Washington, DC, United States
Duration: 5 Oct 20038 Oct 2003

Keywords

  • Adaptive prediction
  • Context modeling
  • Fuzzy clustering
  • Lossless image compression
  • Neural network

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