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Blind image quality assessment through anisotropy.

Salvador Gabarda1, Gabriel Cristóbal

  • 1Instituto de Optica Daza de Valdés (CSIC), Serrano 121, Madrid 28006, Spain.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|December 7, 2007
PubMed
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This study introduces a new image quality assessment method using entropy variance and directionality. It effectively identifies high-quality, in-focus, and noise-free digital images automatically.

Area of Science:

  • Digital Image Processing
  • Information Theory
  • Computer Vision

Background:

  • Assessing digital image quality is crucial for various applications.
  • Existing methods often require reference images or are computationally intensive.
  • There is a need for objective, non-reference image quality metrics.

Purpose of the Study:

  • To develop an innovative, non-reference methodology for digital image quality assessment.
  • To propose an anisotropy index derived from local entropy variance as a quality metric.
  • To demonstrate the effectiveness of this index in identifying high-quality natural images.

Main Methods:

  • Calculating local entropy using generalized Rényi entropy and normalized pseudo-Wigner distribution (PWD).
  • Measuring the variance of expected entropy as a function of directionality to quantify anisotropy.

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  • Employing an oriented 1-D PWD for directional selectivity.

    • Main Results:

    • The proposed anisotropy index serves as a reliable image quality metric.
    • In-focus, noise-free images exhibit a maximum value for this metric.
    • The index successfully distinguishes high-quality images from blurred or noisy ones.
    • The new measure shows good correlation with classical metrics like Peak Signal-to-Noise Ratio (PSNR).

    Conclusions:

    • The developed anisotropy measure is a suitable quality index for natural images.
    • This method enables automatic, non-reference classification of images based on relative quality.
    • The findings offer a promising approach for objective image quality evaluation.