In the 21st century, an informative network era, the Internet has become an important way for people to acquire the latest information and entertainment. Visual information, including images and videos, has accounted for more than 80% of the total Internet traffic. High quality visual experience is the common basis of major applications such as the digital media industry and network information service.
Image quality assessment (IQA), dedicated to evaluating human visual perception and predict image quality, has been a fundamental issue in image processing fields. Although subjective IQA is the most accurate approach, the slowness, time-consuming, laboriousness and difficult repetition of subjective IQA immensely limit its progress. By contrast, objective IQA that resorts to mathematical metrics for predicting the perceived quality of images automatically and efficiently has been widely researched. In common IQA databases, the distorted images are usually degraded from a pristine image called the reference image. According to the available information of the reference image, objective IQA algorithms can be classified into full-reference (FR), reduced-reference (RR) and no-reference (NR) algorithms, respectively.
The human eye is the ultimate evaluator for visual experience, thus the modeling of human visual system (HVS) is a core issue for objective IQA and visual experience optimization. The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively, while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.
For bridging the gap between signal distortion and visual experience, this paper, by Prof. Guangtao Zhai of Shanghai Jiao Tong University, proposes a novel perceptual no-reference (NR) IQA algorithm based on structural computational modeling of HVS. According to the mechanism of the human brain, it divides the visual signal processing into a low-level visual layer, a middle-level visual layer and a high-level visual layer, which conducts pixel information processing, primitive information processing and global image information processing, respectively.
The natural scene statistics (NSS) based features, deep features and free-energy based features are extracted from these three layers. The support vector regression (SVR) is employed to aggregate features to the final quality prediction. Extensive experimental comparisons on three widely used benchmark IQA databases (LIVE, CSIQ and TID2013) demonstrate that the proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.
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Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment
Wen-Han Zhu, Wei Sun, Xiong-Kuo Min, Guang-Tao Zhai, Xiao-Kang Yang
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