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Comparison between adaptive search and bit allocation algorithms for image compression using vector quantization.

K M Liang1, C M Huang, R W Harris

  • 1Dept. of Electr. Eng., Utah State Univ., Logan, UT.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study compares bit allocation algorithms for vector quantization. Adaptive search algorithms offer comparable performance to optimal methods with significantly reduced complexity for image compression.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Data Compression

Background:

  • Vector quantization (VQ) is a widely used technique for data compression.
  • Mean-residual vector quantization (MRVQ) and multistage vector quantization (MSVQ) are specific VQ methods.
  • Efficient bit allocation is crucial for optimizing VQ performance.

Purpose of the Study:

  • To evaluate adaptive search algorithms for bit allocation in MRVQ and MSVQ.
  • To compare the performance and complexity of adaptive search against optimal bit allocation.
  • To analyze the trade-offs between bit rate control and compression efficiency.

Main Methods:

  • Implementation of an adaptive search algorithm using a buffer and distortion threshold.
  • Comparison of adaptive search with optimal bit allocation for fixed codebooks and varying bit rates.

Related Experiment Videos

  • Performance evaluation based on signal-to-noise ratio (dB) and computational complexity.

    • Main Results:

    • The adaptive search algorithm achieves near-optimal performance, with only a 0.20-0.53 dB difference compared to optimal bit allocation.
    • The adaptive algorithm maintains a constant overall image bit rate but allows for variable bit rates per vector.
    • Computational complexity of the adaptive search algorithm is substantially lower than that of the optimal bit allocation algorithm.

    Conclusions:

    • Adaptive search algorithms provide an efficient and practical approach for bit allocation in MRVQ and MSVQ.
    • These algorithms offer a favorable balance between compression performance and computational complexity.
    • The findings suggest that adaptive search is a viable alternative for real-time image compression applications.