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Conditional entropy-constrained residual VQ with application to image coding.

F Kossentini1, W C Chung, M T Smith

  • 1Digital Signal Processing Lab., Georgia Inst. of Technol., Atlanta, GA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study presents conditional entropy-constrained residual vector quantization (VQ), enhancing image coding by exploiting intervector dependencies. This method offers improved rate-distortion performance with reduced complexity and supports progressive transmission.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Information Theory

Background:

  • Traditional entropy-constrained residual vector quantization (VQ) methods often overlook intervector dependencies, limiting compression efficiency.
  • Existing VQ techniques may require significant computational resources and memory, posing challenges for real-time applications.

Purpose of the Study:

  • To introduce a novel conditional entropy-constrained residual VQ (CECR-VQ) method that leverages intervector dependencies.
  • To evaluate the rate-distortion performance, computational complexity, and memory requirements of the proposed CECR-VQ for image coding.
  • To explore the suitability of CECR-VQ for progressive image transmission.

Main Methods:

  • Development of a high-order entropy conditioning strategy to capture local information from neighboring vectors.

Related Experiment Videos

  • Integration of a joint optimization process between the residual vector quantizer and a high-order conditional entropy coder.
  • Implementation of a multistage residual VQ structure with dynamic prediction for efficient coding.

    • Main Results:

    • The proposed CECR-VQ achieves superior rate-distortion performance compared to standard entropy-constrained residual VQ for image coding.
    • CECR-VQ demonstrates reduced computational complexity and lower memory requirements than existing methods.
    • The method naturally supports progressive transmission, offering flexibility in image delivery.

    Conclusions:

    • Conditional entropy-constrained residual VQ is an effective extension of VQ for image compression, outperforming previous techniques.
    • The method's efficiency stems from exploiting intervector dependencies, joint optimization, and an efficient multistage structure.
    • CECR-VQ offers a promising approach for high-performance, low-complexity image coding with progressive transmission capabilities.