Fast Image Quality Assessment via Hash Code

Lihuo He, Qi Liu, Di Wang, Wen Lu

Abstract: No-reference image quality assessment (NR-IQA) is significant for image processing and yet very challenging, especially for real-time application and big image data processing. Traditional NR-IQA metrics usually train complex models such as support vector machine, neural network, and probability graph, which result in long training and testing time and lack robustness. Hence, this paper proposed a novel no-reference image quality via hash code (NRHC). First, the image is divided into some overlapped patches and the features of blind/ referenceless image spatial quality evaluator (BRISQUE) are extracted for each patch. Then the features are encoded to produce binary hash codes via an improved iterative quantization (IITQ) method. Finally, comparing the hash codes of the test image with those of the original undistorted images, the final image quality can be obtained. Thorough experiments on standard databases, e.g. LIVE II, show that the proposed NRHC obtains promising performance for NR-IQA. And it has high computational efficiency and robustness for different databases and different distortions.