@inproceedings{b023902dc8fc4101b34aa258af2515b2,
title = "No-Reference Image Quality Assessment Based on Image Naturalness and Semantics",
abstract = "Automatically providing feedback about the quality of natural images could be of great interest for image-driven applications. Toward this goal, this paper proposes a novel no-reference image quality metric capable of effectively evaluating the image quality without requring the information of the original image. The proposed method delivers a comprehensive analysis of the image quality through exploring its statistical natural properties and high-level semantics. Specifically, we adopt the NSS regularities based method to characterize the image naturalness, and take the semantic information of the image through the deep neural networks. The quality prediction model is then derived by SVR to analyze the relationship between these extracted features and the image quality. Experiments conducted on LIVEC and CID2013 databases manifest the effectiveness of the proposed metric as compared to existing representative image quality assessment techniques.",
keywords = "Deep neural network (DNN), Image quality assessment (IQA), Naturalness",
author = "Runze Hu and Wuzhen Shi and Yutao Liu and Xiu Li",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Singapore Pte Ltd.; 18th International Forum of Digital Multimedia Communication, IFTC 2021 ; Conference date: 03-12-2021 Through 04-12-2021",
year = "2022",
doi = "10.1007/978-981-19-2266-4_16",
language = "English",
isbn = "9789811922657",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "203--214",
editor = "Guangtao Zhai and Jun Zhou and Hua Yang and Ping An and Xiaokang Yang",
booktitle = "Digital TV and Wireless Multimedia Communications - 18th International Forum, IFTC 2021, Revised Selected Papers",
address = "Germany",
}