Hybrid feature similarity approach to full-reference image quality assessment

  • Authors:

  • Krzysztof Okarma

  • Affiliations:

  • Faculty of Electrical Engineering, Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology, Szczecin, Szczecin, Poland

  • Venue:
  • ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

In the paper the Hybrid Feature Similarity metric is proposed based on the combination of two recently proposed objective image quality assessment methods - Riesz transform based Feature Similarity metric and Feature Similarity index. Both of them have good performance in comparison to most "state-of-the-art" quality metrics but highly linear correlation with subjective scores requires an additional nonlinear mapping for tuning to each dataset. In order to overcome this problem and obtain high quality prediction accuracy the nonlinear combination of both metrics is proposed leading to better performance than using each of the metrics separately. The experiments conducted in order to propose the weighting coefficients for both metrics have been performed using TID2008 dataset which is currently the largest and most comprehensive publicly available image quality assessment database, containing 1700 images together with their subjective quality evaluations. The verification of the obtained results has been also conducted using some other relevant benchmark databases.