Spatio-temporal quality assessment for home videos
Proceedings of the 13th annual ACM international conference on Multimedia
Improving relevance judgment of web search results with image excerpts
Proceedings of the 17th international conference on World Wide Web
Where are focused places of a photo?
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Visual quality assessment for web videos
Journal of Visual Communication and Image Representation
Compact representation for large-scale clustering and similarity search
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Where should I stand? Learning based human position recommendation for mobile photographing
Multimedia Tools and Applications
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In this paper, a novel learning based method is proposed for No-Reference image quality assessment. Instead of examining the exact prior knowledge for the given type of distortion and finding a suitable way to represent it, our method aims to directly get the quality metric by means of learning. At first, some training examples are prepared for both high-quality and low-quality classes; then a binary classifier is built on the training set; finally the quality metric of an un-labeled example is denoted by the extent to which it belongs to these two classes. Different schemes to acquire examples from a given image, to build the binary classifier and to model the quality metric are proposed and investigated. While most existing methods are tailored for some specific distortion type, the proposed method might provide a general solution forNo-Reference image quality assessment. Experimental results on JPEG and JPEG2000 compressed images validate the effectiveness of the proposed method.