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Image subband coding using context-based classification and adaptive quantization.

Y Yoo1, A Ortega, B Yu

  • 1Media Technologies Laboratory, DSP Solutions R&D Center, Texas Instruments Inc., Dallas, TX 75243, USA. yoo@ti.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This study introduces a novel backward adaptive quantization method for subband image coding. The technique efficiently adapts to image content, achieving superior rate-distortion performance, especially at low bit rates.

Area of Science:

  • Digital image processing
  • Signal processing
  • Data compression

Background:

  • Adaptive compression is crucial for subband (wavelet) image coding.
  • Current methods often use forward adaptation, requiring significant side information.
  • There's a need for efficient, adaptive image coders that minimize overhead.

Purpose of the Study:

  • To develop a backward adaptive quantization technique for subband image coding.
  • To improve coder adaptability to spatially varying image statistics.
  • To enhance rate-distortion performance, particularly at low bit rates.

Main Methods:

  • Implemented backward adaptive quantization using context-based classification.
  • Classified subband coefficients based on surrounding quantized values.

Related Experiment Videos

  • Employed online parametric adaptation of a uniform threshold quantizer for each class.

    • Main Results:

    • The proposed method demonstrates excellent rate-distortion performance.
    • Performance is particularly strong at very low bit rates.
    • Results are comparable or superior to existing state-of-the-art image coders.

    Conclusions:

    • Backward adaptive quantization with context-based classification offers significant advantages.
    • This approach effectively handles spatially varying image content.
    • The technique provides a competitive solution for efficient subband image coding.