Gaussian Process-based Feature-Enriched Blind Image Quality Assessment |
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Affiliation: | 1. National Cheng Kung University, Tainan, Taiwan;2. National Chung Cheng University, Chiayi, Taiwan;1. Information Science Teaching and Research Section, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;2. College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China |
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Abstract: | The objective of blind-image quality assessment (BIQA) research is the prediction of perceptual quality of images, without reference information. The human’s perceptual assessment of quality of an image is the backbone of BIQA research. Therefore, human-provided, mean opinion score (perceptual quality) has been analyzed in detail, and it has been observed to follow the Gaussian distribution and thus can be ideally modeled by the same. In this paper, we have proposed an integrated two-stage Gaussian process-based hybrid-feature selection algorithm for the BIQA problem. Moreover, a new consolidated feature set (obtained from the proposed algorithm), consisting of momentous Natural Scene Statistics (NSS)-based features is used in combination with the Gaussian process regression algorithm for the design of a new blind-image quality evaluator, referred to as GPR-BIQA. The proposed evaluator is tested on eight IQA legacy databases, and it is found that the proposed evaluator proficiently correlate with the human opinion, and outperformed a substantial number of existing approaches. |
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Keywords: | Image quality assessment (IQA) No-reference (NR) Natural scene statistics Feature selection Gaussian process regression Blind image quality assessment (BIQA) |
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