A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling
Sheng-hua Zhong, Yan Liu, Yan Liu, Fu-Lai Chung · 2010
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.