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

Abstract

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 descriptors 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 ganglion cells. 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.

31 pages, 9 figures, 7 tables

References in corpus (3)

• PerSIM: Multi-resolution Image Quality Assessment in the Perceptually Uniform Color Domain
• Improving Perceptual Color Difference using Basic Color Terms
• Image Quality Assessment and Color Difference

Cited by in corpus (7)

• Tensor Oriented No-Reference Light Field Image Quality Assessment
• Perceptual Image Quality Assessment through Spectral Analysis of Error Representations
• Evaluation of quality measures for color quantization
• Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
• A combined full-reference image quality assessment approach based on convolutional activation maps
• Distorted Representation Space Characterization Through Backpropagated Gradients
• Empirical evaluation of full-reference image quality metrics on MDID database