Fast image quality assessment via supervised iterative quantization method
Fast image quality assessment via supervised iterative quantization method
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DOI：
10.1016/j.neucom.2016.01.116
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发表时间：
2016-11
期刊：
影响因子：
6
通讯作者：
Lihuo He;Di Wang;Qi Liu;Wen Lu
中科院分区：
文献类型：
--
作者：
Lihuo He;Di Wang;Qi Liu;Wen Lu
无参考/盲图像质量评估（NR-IQA/BIQA）对于图像处理非常重要，但也非常具有挑战性，特别是对于实时应用和大图像数据处理。传统的NR-IQA度量通常需要训练复杂的模型，如支持向量机、神经网络、概率图模型等，导致计算时间长、鲁棒性差。为了克服这些缺点，本文提出了一种快速的无参考图像质量评估方法，称为NRHC。首先，将图像分割成重叠的小块，提取自然场景图像的空间统计特征。然后通过监督迭代量化（SITQ）方法对特征进行编码以产生二进制哈希码。最后，计算测试图像的哈希码与原始未失真图像的哈希码之间的汉明距离，以获得最终的图像质量。在基准数据库上的实验表明，与现有的NR-IQA方法相比，该方法具有更高的计算效率和更强的鲁棒性.
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.