@inproceedings{1bebb5b3c6854eb18b891e9b376fa11b,
title = "A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling",
abstract = "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 eyetracking 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.",
keywords = "Image quality assessment, No-reference, Top-down \& bottom-up saliency map",
author = "Zhong, \{Sheng Hua\} and Yan Liu and Yang Liu and Chung, \{Fu Lai\}",
note = "Publisher Copyright: {\textcopyright} 2010 IEEE; 2010 17th IEEE International Conference on Image Processing, ICIP 2010 ; Conference date: 26-09-2010 Through 29-09-2010",
year = "2010",
month = sep,
day = "26",
doi = "10.1109/ICIP.2010.5653807",
language = "English",
isbn = "9781424479948",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE",
pages = "1553--1556",
booktitle = "2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings",
address = "United States",
}