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A complexity reduction technique for image vector quantization.

C K Chan1, L M Po

  • 1Dept. of Electron. Eng., City Polytech. of Hong Kong.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
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This study introduces a new image vector quantization (VQ) method using transform domain distortion measures. This approach enhances efficiency and image quality for real-time image encoding.

Area of Science:

  • Image processing
  • Data compression
  • Signal processing

Background:

  • Spatial-domain image vector quantization (VQ) can be computationally complex.
  • Existing transform-domain VQ methods may reduce codevector dimensions, potentially impacting image quality.

Purpose of the Study:

  • To propose a novel technique for reducing the complexity of spatial-domain image vector quantization (VQ).
  • To improve the efficiency and maintain high image quality in image compression algorithms.

Main Methods:

  • Replaced conventional spatial domain distortion measure with a transform domain subspace distortion measure.
  • Utilized energy compaction properties of image transforms to reduce distortion measure dimensionality.
  • Modified the Linde-Bouton-Gray (LBG) algorithm to incorporate the new distortion measure.

Related Experiment Videos

  • Investigated performance and design for a real-time image encoder.

    • Main Results:

    • Achieved significant reduction in codebook design time and search time for mean residual VQ.
    • Reduced fast RAM size by a factor of four.
    • Maintained high image quality with less than 0.4 dB PSNR degradation compared to spatial domain methods.
    • Guaranteed better image quality by not reducing codevector dimension, unlike some transform-domain VQ.

    Conclusions:

    • The proposed transform domain subspace distortion measure effectively reduces VQ complexity.
    • The modified LBG algorithm offers a computationally efficient and high-quality solution for real-time image compression.
    • This technique presents a viable alternative for efficient image encoding with minimal perceptual loss.