Abstract
Image quality assessment (IQA) model is designed to measure the image quality in consistent with subjective ratings by computational models. In this research, a reliable full reference color IQA model is proposed by combining the Visual saliency with Color appearance (VC) similarity, gradient similarity and chrominance similarity. Two new color appearance indices, vividness and depth, are selected to build the visual saliency similarity map. The structure and chrominance features are characterized by different channels of chosen color space. VC map plays two roles in the proposed model. One is utilized as feature to compute the local quality of distorted image, and the other is as a weight part to reflect the importance of local domain. The novel model is called visual saliency with color appearance and gradient similarity (VCGS). To quantify the specific parameters of VCGS, some experiments are conducted based on the statistical correlation indices. Massive experiments are performed on the publicly available benchmark single and multiple distortion databases, and the commonly evaluation criteria results prove that VCGS works with higher consistency with the subjective evaluations than the other state-of-the-art IQA models for the prediction accuracy. Besides, VCGS maintain a moderate computational complexity. The MATLAB source code of VCGS is publicly available online at https://github.com/AlAlien/VCGS.
Highlights
With the rapid development of color image contents and imaging devices in various kinds of multimedia communication systems, conventional grayscale counterparts are replaced by chromatic ones
These four databases are the most commonly utilized collections in image quality assessment (IQA) research covering a wide range of ordinarily encountered distortions in real world application
In order to evaluate whether a model is able to predict the perception of human observers, the comparisons are made between the calculated scores using the proposed model and the values rated by the observers
Summary
With the rapid development of color image contents and imaging devices in various kinds of multimedia communication systems, conventional grayscale counterparts are replaced by chromatic ones Under such a transition, perceptual image quality assessment (IQA) has played a significant role in numerous visual data processing application [1]. Numerous IQA models have been proposed with better performance based on HVS [3] These better models characterize the structural information, luminance. Geometric features are suitable for grayscale domain with relative insignificant color information and have high computational efficiency They cannot deal with color image with chromatic deviations, because they overlook the impact of chromatic information in visual quality. A novel similarity-based FR-IQA model is introduced without learning procedure, which combines three feature information processing parts, i.e., visual saliency, structure and chrominance features. This model has a moderate complexity and offers better quality predictions, in comparison to the other state-of-the-art models
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