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Area of Science:

  • Computer Vision
  • Signal Processing
  • Image Processing

Background:

  • Real-world image distortions often involve multiple degradation types simultaneously, posing challenges for traditional single-distortion assessment methods.
  • Existing full-reference image quality assessment (FR IQA) metrics may not adequately capture the complex degradations present in multiply distorted images.

Purpose of the Study:

  • To develop a novel FR IQA metric specifically designed for images affected by multiple distortions.
  • To leverage the power of Quaternion Wavelet Transform (QWT) for a more comprehensive analysis of image quality.

Main Methods:

  • Reference and distorted images were decomposed using QWT.
  • Local similarity of both phase and magnitude was computed for each QWT subband.
  • A weighting method incorporating Human Visual System (HVS) characteristics was employed to construct the IQA metric.
  • Quality scores from individual subbands were averaged to yield the final image quality score.

Main Results:

  • The proposed QWT-based FR IQA metric demonstrated superior performance compared to state-of-the-art methods on multiply distorted images.
  • The joint analysis of phase and magnitude information via QWT proved effective in capturing image quality degradation.

Conclusions:

  • The developed QWT-based FR IQA metric offers a robust solution for assessing the quality of multiply distorted images.
  • Incorporating HVS characteristics and QWT provides a more accurate and reliable image quality evaluation framework.