1. Pre-Attention and Spatial Dependency Driven No-Reference Image Quality Assessment
- Author
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Wang Tianshu, Hua Huang, and Lixiong Liu
- Subjects
Visual perception ,Channel (digital image) ,Computer science ,business.industry ,Image quality ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,02 engineering and technology ,Color space ,Computer Science Applications ,Visualization ,Support vector machine ,Distortion ,Signal Processing ,Human visual system model ,0202 electrical engineering, electronic engineering, information engineering ,Media Technology ,RGB color model ,020201 artificial intelligence & image processing ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Abstract
The excessive emulation of the human visual system and the lack of connection between chromatic data and distortion have been the major bottlenecks in developing image quality assessment. To address this issue, we develop a new no-reference (NR) image quality assessment (IQA) metric that accounts for the impact of pre-attention and spatial dependency on the perceived quality of distorted images. The resulting model, dubbed the Pre-attention and Spatial-dependency driven Quality Assessment (PSQA) predictor, introduces the pre-attention theory to emulate early phase visual perception by refining luminance-channel data. Chromatic data are also processed concurrently by transforming images from RGB to the perceptually optimized SCIELAB color space. Considering that the gray-tone spatial dependency matrix conveys important texture properties that are closely related to visual quality, this matrix, as a mathematical solution for subsequent visual process emulation, is calculated along with its statistical features on both gray and color channels. To clarify the influence of different regression procedures on model output, support vector regression and AdaBoosting Back Propagation (BP) neural networks are adopted separately to train the prediction models. We thoroughly evaluated PSQA on four public image quality databases: LIVE, TID2013, CSIQ, and VCL. The experimental results show that PSQA delivers highly competitive performance compared with top-rank NR and full-reference IQA metrics.
- Published
- 2019