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CSV: Image Quality Assessment Based on Color, Structure, and Visual System


This paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics denoted as CSV. In contrast to the majority of existing methods, we quantify perceptual color degradations rather than absolute pixel-wise changes. We use the CIEDE2000, color difference formulation to quantify low-level color degradations and the Earth Mover’s Distance between color name probability vectors to measure significant color degradations. In addition to the perceptual color difference, CSV also contains structural and perceptual differences. Structural feature maps are obtained by mean subtraction and divisive normalization, and perceptual feature maps are obtained from contrast sensitivity formulations of retinal ganglioncells. The proposed quality estimator CSV is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases, and it is always among the top two performing quality estimators in terms of at least ranking, monotonic behavior or linearity.


[Related Publications]

  1. D. Temel and G. AlRegib, “PerSIM: Multi-Resolution Image Quality Assessment in the Perceptually Uniform Color Domain” IEEE ICIP 2015, Québec City, Canada, Sept. 27-30, 2015. [PDF] [PPT (Poster/Slide)] [Bib] [Code]
  2. D. Temel and G. AlRegib, “Image Quality Assessment and Color Difference” 2nd IEEE Global Conference on Signal and Information Processing, Atlanta, USA, Dec. 3-5, 2014. [PDF] [PPT (Poster/Slide)] [Bib] [Code]
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