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A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling

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4 Author(s)
Sheng-hua Zhong ; Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China ; Yan Liu ; Yang Liu ; Fu-lai Chung

This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eye-tracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic meaning from tag information to learn a top-down & bottom-up saliency model based on the eye-tracking data. Empirical validations on standard dataset demonstrate the effectiveness of the proposed model and metric.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010

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