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Proceedings Paper

Multi-modal multi-fractal boundary encoding in object-based image compression
Author(s): Mark S. Schmalz
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Paper Abstract

The compact representation of region boundary contours is key to efficient representation and compression of digital images using object-based compression (OBC). In OBC, regions are coded in terms of their texture, color, and shape. Given the appropriate representation scheme, high compression ratios (e.g., 500:1 ≤ CR ≤ 2,500:1) have been reported for selected images. Because a region boundary is often represented with more parameters than the region contents, it is crucial to maximize the boundary compression ratio by reducing these parameters. Researchers have elsewhere shown that cherished boundary encoding techniques such as chain coding, simplicial complexes, or quadtrees, to name but a few, are inadequate to support OBC within the aforementioned CR range. Several existing compression standards such as MPEG support efficient boundary representation, but do not necessarily support OBC at CR ≥ 500:1 . Siddiqui et al. exploited concepts from fractal geometry to encode and compress region boundaries based on fractal dimension, reporting CR = 286.6:1 in one test. However, Siddiqui's algorithm is costly and appears to contain ambiguities. In this paper, we first discuss fractal dimension and OBC compression ratio, then enhance Siddiqui's algorithm, achieving significantly higher CR for a wide variety of boundary types. In particular, our algorithm smoothes a region boundary B, then extracts its inflection or control points P, which are compactly represented. The fractal dimension D is computed locally for the detrended B. By appropriate subsampling, one efficiently segments disjoint clusters of D values subject to a preselected tolerance, thereby partitioning B into a multifractal. This is accomplished using four possible compression modes. In contrast, previous researchers have characterized boundary variance with one fractal dimension, thereby producing a monofractal. At its most complex, the compressed representation contains P, a spatial marker, and a D value for each monofractal boundary segment, with slight additional overhead indicating an encoding mode. The simplest representation contains P and a pointer into a database of region patterns. Each of these patterns has an associated fractal dimension, thus alleviating storage of segment-specific D values. Contour reconstruction during decompression is guided by the smoothed contour. Analysis of this procedure over a database of 73 images reveals 622:1 ≤ CR ≤ 1,720:1 is typical for natural scenes, demonstrating the utility of our approach.

Paper Details

Date Published: 25 August 2006
PDF: 11 pages
Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 631503 (25 August 2006); doi: 10.1117/12.682825
Show Author Affiliations
Mark S. Schmalz, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 6315:
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
Gerhard X. Ritter; Mark S. Schmalz; Junior Barrera; Jaakko T. Astola, Editor(s)

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