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Conference on Mathematics of Data/image Pattern Recognition, compression and Encryption with Application IX

作者： Schmalz, Mark S. Univ Florida Ctr Comp Vis & Visualizat Gainesville FL 32611 USA

ISBN: (纸本)0819463949

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 [1] support efficient boundary representation, but do not necessarily support OBC at CR >= 500:1 [2]. Siddiqui et al. [3] 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

关键词： object-based image compression representation fractal dimension

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Boundary representation techniques for object-based image compression

Boundary representation techniques for object-based image co...

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Conference on Mathematics of Data/image Coding, compression, and Encryption VI, with Applications

作者： Schmalz, MS Ritter, GX Univ Florida Ctr Comp Vis & Visualizat Gainesville FL 32611 USA

ISBN: (纸本)0819450812

As digital image and video compression technology evolves, it appears that transform based coding or compression techniques will reach asymptotic rate-distortion performance, especially with respect to linear transforms. Although nonlinear compression techniques (for example, based on Markov models) are being developed, progress has been relatively slow due to challenges in theory development. Alternatively, several research teams have produced algorithms for object-based compression (OBC), whereby an image is segmented into areas of similar color or spatial variance. Each segmented region can then be represented in terms of a small region descriptor and somewhat less compact set of boundary descriptors. This paper examines boundary representations and complexity measures for OBC - a previous publication [1], also related to OBC, overviewed region segmentation techniques. Of particular interest ot OBC is the evolution of boundary representations from early approaches such as chain coding or simplicial complexes to transform coded descriptors such as Fourier or wavelet-based representations. These techniques precede more modem methods such as hierarchical transforms (e.g., wavelets) and direct compression of Boolean boundary representations. In this paper, the authors develop boundary compression algorithms based on concepts derived from statistical image compression theory and hierarchical codebook representations. The resulting boundary encoding technique is suitable for a wide range of compression applications, in particular, OBC. Performance criteria include computational complexity of the encoding process, as well as space complexity of intermediate and final boundary representations.

关键词： object-based image compression boundary representation complexity analysis

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Region segmentation techniques for object-based image compression - A review

Region segmentation techniques for object-based image compre...

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7th Conference on Mathematics of Data/image Coding, compression, and Encryption

作者： Schmalz, MS Ritter, GX Univ Florida Ctr Comp Vis & Visualizat Gainesville FL 32611 USA

ISBN: (纸本)0819454990

image compression based on transform coding appears to be approaching an asymptotic bit rate limit for application-specific distortion levels. However, a new compression technology, called object-based compression (OBC) promises improved rate-distortion performance at higher compression ratios. OBC involves segmentation of image regions, followed by efficient encoding of each region's content and boundary. Advantages of OBC include efficient representation of commonly occurring textures and shapes in terms of pointers into a compact codebook of region contents and boundary primitives. This facilitates fast decompression via substitution, at the cost of codebook search in the compression step. Segmentation cose and error are significant disadvantages in current OBC implementations. Several innovative techniques have been developed for region segmentation, including (a) moment-based analysis, (b) texture representation in terms of a syntactic grammar, and (c) transform coding approaches such as wavelet based compression used in MPEG-7 or JPEG-2000. Region-based characterization with variance templates is better understood, but lacks the locality of wavelet representations. In practice, tradeoffs are made between representational fidelity, computational cost, and storage requirement. This paper overviews current techniques for automatic region segmentation and representation, especially those that employ wavelet classification and region growing techniques. Implementational discussion focuses on complexity measures and performance metrics such as segmentation error and computational cost.

关键词： object-based image compression region segmentation complexity analysis

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A block-based MAP segmentation for image compressions

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1998年第5期8卷 592-601页

作者： Won, CS Dongguk Univ Dept Elect Engn Seoul 100715 South Korea

In this paper, a novel block-based image segmentation algorithm using the maximum a posteriori (MAP) criterion is proposed. The conditional probability in the MAP criterion, which is formulated by the Bayesian framework, is in charge of classifying image blocks into edge, monotone, and textured blocks. On the other hand, the a priori probability is responsible for edge connectivity and homogeneous region continuity. After a few iterations to achieve a deterministic MAP optimization, we can obtain a block-based segmented image in terms of edge, monotone, or textured blocks. Then, using a connected block-labeling algorithm. We can assign a number to all connected homogeneous blocks to define an interior of a region. Finally, uncertainty blocks, which are not given any region number yet, are assigned to one of neighboring homogeneous regions by a block-based region-growing method. During this process! we can also check the balance between the accuracy and the cost of the contour coding by adjusting the size of the uncertainty blocks.,Experimental results show that the proposed algorithm yields larger homogeneous regions which are suitable for the object-based image-compression.

关键词： contour coding image segmentation object-based image compression MAP estimation MPEG-4

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