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RRED indices: reduced reference entropic differencing for image quality assessment.

Rajiv Soundararajan1, Alan C Bovik

  • 1Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA. rajivs@utexas.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 1, 2011
PubMed
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This study introduces reduced-reference image quality assessment (QA) algorithms based on image information changes. These novel algorithms accurately predict subjective quality scores with minimal reference data.

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Information Theory

Background:

  • Traditional image quality assessment (QA) often requires full reference images.
  • Developing reduced-reference (RR) QA algorithms is crucial for efficient image analysis.
  • Understanding information change is key to accurate perceptual QA.

Purpose of the Study:

  • To design and evaluate novel reduced-reference image quality assessment (QA) algorithms.
  • To investigate the impact of varying reference information on QA performance.
  • To correlate algorithm performance with subjective quality assessments.

Main Methods:

  • Algorithms measure entropy differences in wavelet coefficients between reference and distorted images.
  • Entropy calculations utilize varying amounts of information from the reference image.

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  • Performance evaluated using standard image quality databases (LIVE, TID).

    • Main Results:

    • Developed RR-QA algorithms demonstrate strong correlation with subjective quality scores.
    • Performance degradation is minimal even with significantly reduced reference information.
    • Algorithms requiring minimal reference data, including a single number, show promising results.

    Conclusions:

    • Novel RR-QA algorithms based on information change are effective and efficient.
    • These algorithms offer a viable solution for perceptual image quality evaluation with limited reference data.
    • The findings support the development of practical, low-complexity QA systems.