Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm

Sitaram, Vijay S.; Huang, Chien-Min; Israelsen, Paul

doi:10.1109/26.328984

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…However, training times for the neural net are typically long. Additional variants of the Lloyd algorithm have been proposed that utilize stochastic clustering techniques to produce a slightly more compact codebook than Lloyd's greedy algorithm [21,22].…”

Section: Remark Horizontal and Vertical Block Partitioning (mentioning

confidence: 99%

<title>Automated detection and recognition of small targets in compressed imagery: background and theory</title>

Schmalz

1996

SPIE Proceedings

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The detection of small targets in uncompressed imagery frequently incurs high computational cost due to areabased filtering and template matching processes. In particular, the convolution of a K-pixel filter with an N-pixel image typically requires work that is bounded below by O(KN). However, we have shown that such image-template operations can be computed in less than O(KN) time if the image is appropriately compressed [1][2][3][4][5]. We call this technique compressive processing.In this two-part series of papers, we present supporting theory, derivations, and analyses of compressive imagetemplate operations that frequently occur in automated target recognition (ATR) practice. For example, compression ratios (CRs) of 30:1 or greater have been reported for imagery where interframe differences are small [6]. Similarly high compression ratios have been reported for video imagery using vector quantization (VQ) or visual pattern image coding (VPIC). We thus derive image operations such as edge detection and target classification that are applicable to VQ-and VPIC-compressed imagery, as well as to a VPIC-like transform, called Adaptive Vector Entropy Coding (AVEC).In the case of edge detection and target classification over VQ-or VPIC-compressed imagery, we show that computational speedups of O(CR) can be obtained with appropriate data structural manipulation. For example, if VQ is employed with fixed-size, K-pixel encoding blocks, then edge detection can be achieved by entropy-based thresholding of the VQ codebook exemplars, at a cost of N/K block substitution operations. Given a codebook of size M vectors, an additional overhead of 2M comparisons may be required for validation purposes. A similar method is employed for VPIC, which encodes image patterns in terms of the encoding block mean, gradient intensity and orientation, and an index that references a bitmap pattern. In practice, the bitmap is derived from the encoding block's zero crossings about the block mean.Analyses emphasize performance measures such as computational cost, information loss, computational error, and compression ratio. Our algorithms are expressed in terms of image algebra [7] , a rigorous, concise notation that unifies linear and nonlinear mathematics in the image domain. Since image algebra has been implemented on numerous sequential and parallel computers, our algorithms are feasible and widely portable.

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Section: Remark Horizontal and Vertical Block Partitioning (mentioning

confidence: 99%

<title>Automated detection and recognition of small targets in compressed imagery: background and theory</title>

Schmalz

1996

SPIE Proceedings

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“…Image data compression using vector quantization(lossy compression) has received a considerable attention because of its simplicity and adaptability [4]. The first empirical design scheme is suggested by Linde,Buzo, and Gray [2], and thus named LBG algorithm, This scheme is a generalization of Lioyd PCM design technique [1].Vector quantization(VQ) requires the input image to be processed as vectors and finds the best or closest match, based on some distortion criterion, from its stored codebook.…”

Section: Introductionmentioning

confidence: 99%

“…The decoder accesses an entry from an identical codebook, thus obtaining the reconstructed vector. Data compression is achieved in this process because the transmission of the address requires fewer bits than transmitting the vector itself [4]. A review of vector quantization techniques used for image coding is presented by Nasrabadi and King [3].…”

Section: Introductionmentioning

confidence: 99%

New Method to Reduce the Size of Codebook in Vector Quantization of Images

Ahmed¹

2005

AL-Rafidain Journal of Computer Sciences and Mathematics

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The vector quantization method for image compression inherently requires the generation of a codebook which has to be made available for both the encoding and decoding processes. That necessitates the attachment of this codebook when a compressed image is stored or sent. For the purpose of improving the overall efficiency of the vector quantization method, the need arose for improving a means for the reduction of the codebook size.In this paper, a new method for vector quantization is presented by which the suggested algorithm reduces the size of the codebook generated in vector quantization. This reduction is performed by sorting the codewords of the codebook then the differences between adjacent codewords are computed. Huffman coding (lossless compression) is performed on the differences in order to reduce the size of the codebbook.

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“…The idea of improving image prediction and coding efficiency by relaxing the neighborhood constraint can be traced back to sequential data compression [3], [4] and vector quantization for image compression [5]. In sequential data compression, a substring of text is represented by a displacement/length reference to a substring previously seen in the text.…”

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confidence: 99%

Lossless Image Compression Using Super-Spatial Structure Prediction

Zhao

2010

IEEE Signal Process. Lett.

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Abstract-We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structure components, such as edges, patterns, and textures. In this work, we develop an efficient lossless image compression scheme called super-spatial structure prediction. This super-spatial prediction is motivated by motion prediction in video coding, attempting to find an optimal prediction of structure components within previously encoded image regions. We find that this super-spatial prediction is very efficient for image regions with significant structure components. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art lossless image compression methods.Index Terms-Context-based adaptive lossless image coding (CALIC), lossless image compression, structure components, super-spatial structure prediction.

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Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm

Cited by 14 publications

References 12 publications

<title>Automated detection and recognition of small targets in compressed imagery: background and theory</title>

<title>Automated detection and recognition of small targets in compressed imagery: background and theory</title>

New Method to Reduce the Size of Codebook in Vector Quantization of Images

Lossless Image Compression Using Super-Spatial Structure Prediction

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