@inproceedings{a23dd0694e98490ca4e1aefb0f14f770,
title = "Image quality assessment based on Structure Similarity",
abstract = "Image Structure Similarity (SSIM) and its extended versions have been successfully used in image quality assessment. In this paper, we propose a similarity metric to evaluate image quality by extracting image sparse structure from natural scene image. A sparse dictionary trained on the data contains the basic elements for representing sparse structures, and it is insensitive to different databases. The sparse structure similarity of testing image pairs is calculated with this dictionary. The final score of image quality is obtained by counting the changed number of elements in sparse structure vector between distorted image and reference image. Experiments demonstrate that the proposed method could assess image quality effectively and outperform existing SSIM based methods.",
keywords = "dictionary learning, full-reference assessment, Image quality, sparse structure",
author = "Jun Wu and Huifang Li and Zhaoqiang Xia",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2016 ; Conference date: 05-08-2016 Through 08-08-2016",
year = "2016",
month = nov,
day = "22",
doi = "10.1109/ICSPCC.2016.7753620",
language = "英语",
series = "ICSPCC 2016 - IEEE International Conference on Signal Processing, Communications and Computing, Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSPCC 2016 - IEEE International Conference on Signal Processing, Communications and Computing, Conference Proceedings",
}