Fast image quality assessment via supervised iterative quantization method

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• 作者：Lihuo HeAuthor Vitae ; Di WangAuthor Vitae ; Qi LiuAuthor Vitae ; Wen Lu ; ^{luwen.xidian@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：No-reference ; Image quality assessment ; Supervised iterative quantization ; Hash code
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 November 2016
• 年：2016
• 卷：212
• 期：Complete
• 页码：121-127
• 全文大小：1064 K

文摘

No-reference/Blind image quality assessment (NR-IQA/BIQA) 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 model, which result in long computational time and poor robustness. To overcome these weaknesses, the paper proposes a fast no-reference image quality assessment via hash coding method, named NRHC. First, the image is divided into overlapped patches to extract the spatial statistical features of natural scene images. Then the features are encoded to produce binary hash codes via supervised iterative quantization (SITQ) method. Finally, the Hamming distances between the hash code of the test image and those of original undistorted images are calculated to obtain the final image quality. Thorough experiments on benchmark databases demonstrate that the proposed approach achieves comparable performance and has higher computational efficiency and stronger robustness compared with the state-of-the-art NR-IQA methods.