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Full-Reference Image Quality Assessment with Linear Combination of Genetically Selected Quality Measures.

Mariusz Oszust1

  • 1Department of Computer and Control Engineering, Rzeszow University of Technology, Rzeszow, Poland.

Plos One
|June 25, 2016
PubMed
Summary

Developing algorithms for automatic image quality assessment (IQA) is crucial. This study proposes a novel approach combining multiple IQA methods to achieve human-consistent image quality evaluation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Image information can be distorted through electronic storage and communication.
  • Automatic image quality assessment (IQA) algorithms are needed to align with human perception.
  • Existing IQA methods may not consistently reflect subjective quality.

Purpose of the Study:

  • To develop a robust image quality assessment approach.
  • To create a joint model that integrates multiple IQA algorithms.
  • To ensure automated quality evaluation aligns with human judgment.

Main Methods:

  • Proposed an IQA approach using a weighted sum of multiple IQA measures.
  • Defined an optimization problem to minimize the error between objective and subjective scores.

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  • Employed a genetic algorithm to determine optimal weights and select relevant IQA measures.
  • Evaluated the joint model on four large image benchmarks.

    • Main Results:

    • The proposed joint multimeasure IQA approach demonstrated superior performance.
    • Outperformed existing state-of-the-art full-reference IQA methods in evaluations.
    • Achieved image quality assessment consistent with human observers.

    Conclusions:

    • Combining multiple IQA measures through optimization significantly enhances assessment accuracy.
    • The genetic algorithm effectively optimizes the aggregation of diverse IQA techniques.
    • The developed approach offers a more reliable and human-aligned method for image quality evaluation.