Fast full search equivalent encoding algorithms for image compression using vector quantization

Huang, Chien-Min; Bi, Qi; Stiles, G. S.; Harris, Richard W.

doi:10.1109/83.148613

Cited by 120 publications

(59 citation statements)

References 15 publications

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“…Similar procedures can also be readily used with the octree quantization scheme. Note however, that if the LBG algorithm is used for iterative improvement of the obtained palette, the tree structure of the palette is destroyed, and therefore the nearest-neighbor search is more involved though fast searching schemes can still be developed [257], [258].…”

Section: A Color Quantizationmentioning

confidence: 99%

Digital color imaging

Sharma

Trussell

1997

IEEE Trans. on Image Process.

367

200

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Abstract-This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.

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Section: A Color Quantizationmentioning

confidence: 99%

Digital color imaging

Sharma

Trussell

1997

IEEE Trans. on Image Process.

367

200

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“…Furthermore, the million connections per second (MCPS) [16], which is a common metric to evaluate the performance of SOM, is also calculated according to Equation (8) and then plotted in Figure 8. Likewise, in Equation (8) Semiconductor)process, a higher work frequency can be reached, and thus a faster compression speed will be obtained accordingly.…”

Section: Speed Analysismentioning

confidence: 99%

“…Likewise, in Equation (8), d and N represent the pixel-block size and the codebook size, respectively. The results indicate that the value of MCPS reaches 28,494 when the size of pixel-block is 8 × 8 (compression ratio is 64) and the sub-block number is 32.…”

Section: = ×mentioning

confidence: 99%

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A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression

et al. 2017

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This paper presents a compact vector quantizer based on the self-organizing map (SOM), which can fulfill the data compression task for high-speed image sequence. In this vector quantizer, we solve the most severe computational demands in the codebook learning mode and the image encoding mode by a reconfigurable complete-binary-adder-tree (RCBAT), where the arithmetic units are thoroughly reused. In this way, the hardware efficiency of our proposed vector quantizer is greatly improved. In addition, by distributing the codebook into the multi-parallel processing sub-blocks, our design obtains a high compression speed successfully. Furthermore, a mechanism of partial vector-component storage (PVCS) is adopted to make the compression ratio adjustable. Finally, the proposed vector quantizer has been implemented on the field programmable gate array (FPGA). The experimental results indicate that it respectively achieves a compression speed of 500 frames/s and a million connections per second (MCPS) of 28,494 (compression ratio is 64) when working at 79.8 MHz. Besides, compared with the previous scheme, our proposed quantizer achieves a reduction of 8% in hardware usage and an increase of 33% in compression speed. This means the proposed quantizer is hardware-efficient and can be used for high-speed image compression.

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“…For big sets of vectors and large code-books however, an exhaustive search would require an enormous number of operations, due to elevate number of needed comparisons. For these reasons, in this work the techniques for a fast search returning the same results of an exhaustive one have been implemented as in [49]. These methods obtain a reduced computational complexity due to geometric considerations: each vector is compared only with the code-words belonging to a specific region, which has been determined by observing the spatial distribution of the code-words with respect to the vector to be quantized.…”

Section: Tsvq Computational Complexitymentioning

confidence: 99%

Hierarchical structuring of video previews by Leading-Cluster-Analysis

Benini

Migliorati

Leonardi

2010

SIViP

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Clustering of shots is frequently used for accessing video data and enabling quick grasping of the associated content. In this work we first group video shots by a classic hierarchical algorithm, where shot content is described by a codebook of visual words and different codebooks are compared by a suitable measure of distortion. To deal with the high number of levels in a hierarchical tree, a novel procedure of Leading-Cluster-Analysis is then proposed to extract a reduced set of hierarchically arranged previews. The depth of the obtained structure is driven both from the nature of the visual content information, and by the user needs, who can navigate the obtained video previews at various levels of representation. The effectiveness of the proposed method is demonstrated by extensive tests and comparisons carried out on a large collection of video data.

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Fast full search equivalent encoding algorithms for image compression using vector quantization

Cited by 120 publications

References 15 publications

Digital color imaging

Digital color imaging

A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression

Hierarchical structuring of video previews by Leading-Cluster-Analysis

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