Adaptive vector quantisation of non-orthogonal representations for image compression

Mikhael, W.B.; Ragothaman, P.

doi:10.1049/el:20030139

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Therefore, the proposed PCU-AVQ algorithm can be simplified as follows: 1:Initialize the codebook P, and Lagrangian multiplier λ. Calculate T * according to (4): T * = λ/0.10 2. Find the nearest codewordp for the input vector s based on the Euclidean norm: s −p 2 = min s − p i 2 3: Generate the partial updated codewordp(T * ) and record …”

Section: Relationship Between Updating Threshold and Lagrangian Multimentioning

confidence: 99%

“…VQ is theoretically attractive; however, there still exists a large gap between the theoretical performance and actually achieved performance. Thus, many adaptive vector quantization (AVQ) algorithms [2], [3], [4] were proposed. The most important feature of AVQ is that codebook can be updated to track the changing statistics of data source in the coding process [2].…”

Section: Introductionmentioning

confidence: 99%

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A New Partial Codeword Updating Scheme Based on Rate-Distortion Optimization for Adaptive Vector Quantization

Guo

2007

Multimedia and Expo, 2007 IEEE International Conference On

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In this paper, we propose a new adaptive vector quantization (AVQ) algorithm based on the rate-distortion optimization. This algorithm employs a new partial codeword updating (PCU) scheme which achieves rate-distortion performance superior to that of the conventional AVQ algorithms using the full codeword updating (FCU) scheme. The PCU-AVQ only updates the codeword's components with the quantization error higher than an optimal threshold instead of replacing the whole codeword. Additionally, the mathematical relation between the Lagrangian multiplier and the approximate optimal threshold is devised to reduce the ratedistortion cost computation. The experimental results show that the proposed PCU-AVQ algorithm indeed improves the rate-distortion performance without much computational complexity penalty. The PCU-AVQ can be combined with transform coding and entropy coding for higher compression ratio, and it can be widely implemented in specific AVQ algorithms for image, video and speech coding.Index Terms-Adaptive vector quantization, image and video coding, rate-distortion optimization

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Section: Relationship Between Updating Threshold and Lagrangian Multimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Partial Codeword Updating Scheme Based on Rate-Distortion Optimization for Adaptive Vector Quantization

Guo

2007

Multimedia and Expo, 2007 IEEE International Conference On

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A Pattern-Based Residual Vector Quantization (PBRVQ) algorithm for compressing images

Somasundaram

Rani

2009

2009 International Conference on the Current Trends in Information Technology (CTIT)

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A pattern-based residual vector quantization (PBRVQ) algorithm for compressing images

Somasundaram

Rani

2009

2009 International Conference on High Performance Computing &Amp; Simulation

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Adaptive vector quantisation of non-orthogonal representations for image compression

Cited by 3 publications

References 3 publications

A New Partial Codeword Updating Scheme Based on Rate-Distortion Optimization for Adaptive Vector Quantization

A New Partial Codeword Updating Scheme Based on Rate-Distortion Optimization for Adaptive Vector Quantization

A Pattern-Based Residual Vector Quantization (PBRVQ) algorithm for compressing images

A pattern-based residual vector quantization (PBRVQ) algorithm for compressing images

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