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Adaptive image coding with perceptual distortion control.

Ingo Höntsch1, Lina J Karam

  • 1Inst. fur Rundfunktechnik, Munich. hontsch@irt.de

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
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This study introduces a novel image coder using discrete cosine transform (DCT) and adaptive perceptual methods. It enhances image quality and reduces bit rate by exploiting human visual masking properties.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Perceptual image coding aims to optimize visual quality and compression efficiency.
  • Existing methods often lack adaptability to local image characteristics.
  • Human visual system properties, like masking, are crucial for efficient coding.

Purpose of the Study:

  • To develop a locally adaptive perceptual image coder based on Discrete Cosine Transform (DCT).
  • To improve image quality and bit rate performance by exploiting human visual masking.
  • To introduce a tractable perceptual distortion metric for adaptive quantization.

Main Methods:

  • Utilizing a DCT-based framework for image decomposition.
  • Implementing a locally adaptive perceptual quantization scheme.

Related Experiment Videos

  • Deriving visual masking thresholds adaptively based on local image content.
  • Controlling quantization reconstruction levels to meet target perceptual distortion.

    • Main Results:

    • The proposed coder demonstrates superior performance over existing perceptual methods.
    • Achieved enhanced control over bit rate and perceptual distortion.
    • The scheme is flexible and extendable to various decomposition methods.

    Conclusions:

    • The DCT-based locally adaptive perceptual coder offers significant improvements in image compression.
    • Exploiting human visual masking adaptively is key to efficient perceptual coding.
    • The method provides a robust and flexible approach for advanced image compression.