Self-Describing context-based pixel ordering

Authors:
Abdul Itani;Manohar Das
Affiliations:
Oakland University, Rochester, Michigan;Oakland University, Rochester, Michigan
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

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Abstract

In this paper we introduce a novel self-describing context-based pixel ordering for digital images. Our method is inherently reversible and uses the pixel value to guide the exploration of the two-dimensional image space, in contrast to universal scans where the traversal is based solely on the pixel position. The outcome is a one-dimensional representation of the image with enhanced autocorrelation. When used as a front-end to a memoryless entropy coder, empirical results show that our method, on average, improves the compression rate by 11.56% and 5.23% compared to raster-scan and Hilbert space-filling curve, respectively.