Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/38133
Title: Learning without human scores for blind image quality assessment
Authors: Xue, W
Zhang, L 
Mou, X
Keywords: Bind image quality assessment
Clustering
Qualiyt aware
Issue Date: 2013
Source: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23-28 June 2013, Portland, OR, p. 995-1002 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/38133
ISSN: 1948-5719
DOI: 10.1109/CVPR.2013.133
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