&lt;title&gt;Techniques for region coding in object-based image compression&lt;/title&gt;

Mathematics of Data/Image Coding, Compression, and Encryption VIII, With Applications

Ritter

2005

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Image compression via transform coding applied to small rectangular regions or encoding blocks appears to be approaching asymptotic rate-distortion performance. However, an emerging compression technology, called object-based compression (OBC) promises significantly improved performance via compression ratios ranging from 200:1 to as high as 2,500:1. OBC involves segmentation of image regions, followed by efficient encoding of each region's content and boundary. During decompression, such regions can be approximated by objects from a codebook, yielding a reconstructed image that is semantically equivalent to the corresponding source image, but has pixel-and featural-level differences.Semantic equivalence between the source and decompressed image facilitates fast decompression through efficient substitutions, albeit at the cost of codebook search in the compression step. Given small codebooks, OBC holds promise for information-push technologies where approximate context is sufficient, for example, transmission of surveillance images that provide the gist of a scene. However, OBC is not necessarily useful for applications requiring high accuracy, such as medical image processing, because substitution of source content can be inaccurate at small spatial scales.The cost of segmentation is a significant disadvantage in current OBC implementations. Several innovative techniques have been developed for region segmentation, as discussed in a previous paper [4]. Additionally, tradeoffs between representational fidelity, computational cost, and storage requirement occur, as with the vast majority of lossy compression algorithms. This paper analyzes the computational (time) and storage (space) complexities of several recent OBC algorithms applied to single-frame imagery. A time complexity model is proposed, which can be associated theoretically with a space complexity model that we have previously published [2]. The result, when combined with measurements of representational accuracy described in a companion paper [5], supports estimation of a time-space-error bandwidth product that could facilitate dynamic optimization of OBC algorithms. In practice, this would support efficient compression with visually acceptable reconstruction for a wide variety of military and domestic applications.

Section: Introductionmentioning

confidence: 94%

“…Unlike classical VQ, object-based compression transforms parameterize the substitution with scaling factors and offsets [1][2][3][4], similar in concept to iterated function systems (IFS) [7]. Whereas VQ typically employs fixed-size blocks, regions in object compression can be arbitrarily shaped.…”

Section: Introductionmentioning

confidence: 99%

<title>Computational complexity of object-based image compression</title>

Mathematics of Data/Image Coding, Compression, and Encryption VIII, With Applications

Ritter

2005

Self Cite

“…However, such techniques are not of primary interest to this study, which focuses on simplified, efficient metrics. The computational work required by comparison in an L-band color space using the weighted Euclidean distance given in Equation (6) is expressed as W (L) = 2L multiplications + (2L -1) additions + 1 square root operation (8) per region. If image a contains n a regions, and d contains n d regions, then it would naively seem that n a · n d comparisons would be required.…”

Section: Color Comparisonmentioning

confidence: 99%

“…The authors have published OBC algorithms that can achieve high compression ratios (typically, 200:1 < CR < 2500:1), high computational efficiency due to the efficient substitutions employed in decompression, and semantic accuracy [7][8][9]. This holds especially for scenes that have several detailed objects which can be represented, for example, by textures stored in a small codebook of exemplars.…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

<title>Error measures for object-based image compression</title>

Mathematics of Data/Image Coding, Compression, and Encryption VIII, With Applications

Ritter

2005

Self Cite

Image compression based on transform coding appears to be approaching a bit-rate limit for visually acceptable distortion levels. Although an emerging compression technology called object-based compression (OBC) promises significantly improved bit rate and computational efficiency, OBC is epistemologically distinct in a way that renders existing image quality measures (IQMs) for compression transform optimization less suitable for OBC. In particular, OBC segments source image regions, then efficiently encodes each region's content and boundary. During decompression, region contents are often replaced by similar-appearing objects from a codebook, thus producing a reconstructed image that corresponds semantically to the source image, but has pixel-, featural-, and object-level differences that are apparent visually. OBC thus gains the advantage of fast decompression via efficient codebook-based substitutions, albeit at the cost of codebook search in the compression step and significant pixel-or region-level errors in decompression. Existing IQMs are pixel-and regionoriented, and thus tend to indicate high error due to OBC's lack of pixel-level correlation between source and reconstructed imagery. Thus, current IQMs do not necessarily measure the semantic correspondence that OBC is designed to produce. This paper presents image quality measures for estimating semantic correspondence between a source image and a corresponding OBC-decompressed image. In particular, we examine the semantic assumptions and models that underlie various approaches to OBC, especially those based on textural as well as high-level name and spatial similarities. We propose several measures that are designed to quantify this type of high-level similarity, and can be combined with existing IQMs for assessing compression transform performance. Discussion also highlights how these novel IQMs can be combined with time and space complexity measures for compression transform optimization.

On the convergence of image compression and object recognition

Proceedings of the 43rd Annual Southeast Regional Conference - Volume 2

2005

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Over the past four decades, image compression and object recognition have evolved from pixel-level to region-level processing, and thence to feature-based resolution. For example, compression has progressed from entropy coding of a bit or pixel stream, to transform coding applied to rectangular encoding blocks, to feature-based compression that employs segmentation of isospectral or isotextural regions. Similarly, object recognition has progressed from operations on individual pixel intensities or spectral signatures, to region-or feature-based processing employing segmentation. Recent progress in image compression indicates that significantly decreased bit rate (thus, significantly increased compression ratio) can be obtained by isolating or segmenting, then compressing scene objects, which is called object-based compression or OBC.In this paper, the relationship between object segmentation and compression is presented theoretically, then exemplified in terms of current research in OBC. The relationship between object compression and recognition is also discussed theoretically. Recent work in object recognition is shown to be closely related theoretically to OBC. Two recently-developed paradigms, Muñoz et al.'s region growing algorithm and Campbell et al.'s QuickSketch, which support very efficient compression and accurate recognition/retrieval of image objects, are summarized. Additionally, a performance model is given for OBC that isolates space complexity bottlenecks for future enhancement and implementation.