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A complete no-reference image quality assessment method based on local feature

ABSTRACT Image quality assessment (IQA) is widely used in digital image processing, and no-reference (NR) IQA has become research focus recently. This paper proposes a NR IQA method based on local features without access to prior knowledge of the images or their distortions. Four gradient masks are used to detect the maximum local gradient (MLG), and the analysis shows that the MLG of strong structure (such as region boundary) includes very tiny noise component, thus this paper assesses image visual quality by using MLGs of strong structures. The proposed method can assess noisy image and blurred image at the same time, and the quality score drops either when the test image becomes blurred or corrupted by random noise. The experiment results show that the proposed approach works well on LIVE, TID2013 and CSIQ databases, and it outperforms some state-of-the-art algorithms.