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A color intensity invariant low-level feature optimization framework for image quality assessment

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Abstract

Image quality assessment (IQA) algorithms evaluate the perceptual quality of an image using evaluation scores that assess the similarity or difference between two images. We propose a new low-level feature-based IQA technique, which applies filter-bank decomposition and center-surround methodology. Differing from existing methods, our model incorporates color intensity adaptation and frequency scaling optimization at each filter-bank level and spatial orientation to extract and enhance perceptually significant features. Our computational model exploits the concept of object detection and encapsulates characteristics proposed in other IQA algorithms in a unified architecture. We also propose a systematic approach to review the evolution of IQA algorithms using unbiased test datasets, instead of looking at individual scores in isolation. Experimental results demonstrate the feasibility of our approach.

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Authors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Navaneeth K. Kottayil, Irene Cheng & Anup Basu

  2. Telecom ParisTech, Paris, France

    Frederic Dufaux

Authors
  1. Navaneeth K. Kottayil
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  2. Irene Cheng
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  3. Frederic Dufaux
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  4. Anup Basu
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Correspondence to Irene Cheng.

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Kottayil, N.K., Cheng, I., Dufaux, F. et al. A color intensity invariant low-level feature optimization framework for image quality assessment. SIViP 10, 1169–1176 (2016). https://doi.org/10.1007/s11760-016-0873-x

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