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Linear, worst-case estimators for denoising quantization noise in transform coded images.

Onur G Guleryuz1

  • 1DoCoMo Communications Laboratories USA Inc, Palo Alto, CA 94304, USA. guleryuz@docomolabs-usa.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 7, 2006
PubMed
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This study introduces robust signal processing for denoising transform-coded images, addressing distortions beyond traditional methods. The new approach optimizes image estimators for worst-case scenarios, improving quantization artifact removal.

Area of Science:

  • Digital Signal Processing
  • Image Processing
  • Robust Statistics

Background:

  • Transform coding introduces distortions that challenge conventional image denoising algorithms.
  • Traditional denoising relies on statistical image models, but transform coding artifacts depend on the transform's structure.

Purpose of the Study:

  • To develop linear, worst-case estimators for denoising transform-coded images using robust signal processing.
  • To create a method that accounts for transform-specific distortions and image statistics.

Main Methods:

  • Constructed linear, worst-case estimators based on simple image and quantization error models.
  • Derived optimal linear estimators minimizing mean-squared error for worst-case cross-correlation.
  • Developed a transform-agnostic approach applicable to various transforms like DCT and wavelets.

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Main Results:

  • The method effectively identifies and removes quantization artifacts from general signals coded with general transforms.
  • A lookup table-based estimator offers competitive Peak Signal-to-Noise Ratio (PSNR) values in low-complexity environments.
  • The approach provides insights into source and quantizer interactions, aiding transform/quantizer design.

Conclusions:

  • The proposed robust signal processing framework offers a versatile solution for denoising transform-coded images.
  • Decoupling modeling and processing allows for adaptable estimators based on computational complexity.
  • This work advances the removal of quantization artifacts, enhancing image quality across diverse coding schemes.