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Image compression in signal-dependent noise.

R Shahnaz1, J F Walkup, T F Krile

  • 1Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas 78711-1084, USA.

Applied Optics
|March 8, 2008
PubMed
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This study shows how signal-dependent noise (SDN) impacts JPEG image compression. Preprocessing noisy images using specific noise estimators can improve compression ratios.

Area of Science:

  • Digital Image Processing
  • Signal Processing
  • Information Theory

Background:

  • Image compression performance is degraded by noise.
  • Signal-dependent noise (SDN), like film-grain and speckle, significantly reduces achievable compression ratios.
  • Preprocessing noisy images for noise suppression is crucial before applying compression.

Purpose of the Study:

  • To investigate the impact of specific signal-dependent noise (SDN) sources on JPEG image compression.
  • To compare two noise suppression approaches for improving compression ratios.
  • To evaluate compression performance on noiseless, noisy, and noise-reduced images.

Main Methods:

  • Investigated JPEG compression performance with film-grain and speckle noise.
  • Employed two noise suppression techniques: SDN-specific estimation and transformation to signal-independent noise (SIN) followed by SIN estimation.

Related Experiment Videos

  • Compared compression results from both methods and with original images.

    • Main Results:

    • Noise significantly reduces image compression performance.
    • Both noise suppression methods showed potential for improving compression ratios.
    • Performance comparison between the two noise suppression schemes was detailed.

    Conclusions:

    • Effective noise suppression is essential for optimizing image compression, particularly in the presence of signal-dependent noise.
    • The choice of noise suppression strategy impacts the final compression efficiency.
    • Further research can explore advanced estimators for enhanced image compression.