Learning without Human Scores for Blind Image Quality Assessment
Learning without Human Scores for Blind Image Quality Assessment
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DOI：
10.1109/cvpr.2013.133
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Wufeng Xue;Lei Zhang;X. Mou
中科院分区：
文献类型：
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作者：
Wufeng Xue;Lei Zhang;X. Mou
通用盲图像质量评估（BIQA）最近在图像处理、视觉和机器学习领域引起了广泛关注。最先进的 BIQA 方法通常通过对训练样本的人类主观评分进行回归来学习评估图像质量。然而，这些方法需要大量的人类评分图像进行训练，并且缺乏对图像局部特征如何影响图像质量的明确解释。那么一个有趣的问题是：我们可以在不使用人类评分图像的情况下学习有效的 BIQA 吗？本文尽力回答这个问题。我们将失真图像划分为重叠的补丁，并使用百分位数池化策略来估计每个补丁的局部质量。然后提出了一种质量感知聚类（QAC）方法来学习每个质量级别上的一组质心。然后将这些质心用作密码本来推断给定图像中每个补丁的质量，随后可以获得整个图像的感知质量得分。所提出的基于 QAC 的 BIQA 方法简单而有效。它不仅具有与使用人类评分图像进行学习的方法相当的准确性，而且具有人类对图像质量感知的高线性度、图像局部质量图的实时实现和可用性等优点。
General purpose blind image quality assessment (BIQA) has been recently attracting significant attention in the fields of image processing, vision and machine learning. State-of-the-art BIQA methods usually learn to evaluate the image quality by regression from human subjective scores of the training samples. However, these methods need a large number of human scored images for training, and lack an explicit explanation of how the image quality is affected by image local features. An interesting question is then: can we learn for effective BIQA without using human scored images? This paper makes a good effort to answer this question. We partition the distorted images into overlapped patches, and use a percentile pooling strategy to estimate the local quality of each patch. Then a quality-aware clustering (QAC) method is proposed to learn a set of centroids on each quality level. These centroids are then used as a codebook to infer the quality of each patch in a given image, and subsequently a perceptual quality score of the whole image can be obtained. The proposed QAC based BIQA method is simple yet effective. It not only has comparable accuracy to those methods using human scored images in learning, but also has merits such as high linearity to human perception of image quality, real-time implementation and availability of image local quality map.