@inproceedings{e85465e2ba994626b93b527e212fd2af,
title = "Image quality assessment using similar scene as reference",
abstract = "Most of Image Quality Assessment (IQA) methods require the reference image to be pixel-wise aligned with the distorted image, and thus limiting the application of reference image based IQA methods. In this paper, we show that non-aligned image with similar scene could be well used for reference, using a proposed Dual-path deep Convolutional Neural Network (DCNN). Analysis indicates that the model captures the scene structural information and non-structural information {\textquotedblleft}naturalness{\textquotedblright} between the pair for quality assessment. As shown in the experiments, our proposed DCNN model handles the IQA problem well. With an aligned reference image, our predictions outperform many state-of-the-art methods. And in more general case where the reference image contains the similar scene but is not aligned with the distorted one, DCNN could still achieve superior consistency with subjective evaluation than many existing methods that even use aligned reference images.",
keywords = "Dual-path deep convolution neural network, Image quality assessment, Similar scene referenced image, Structural similarity, {\textquotedblleft}Naturalness{\textquotedblright}",
author = "Yudong Liang and Jinjun Wang and Xingyu Wan and Yihong Gong and Nanning Zheng",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46454-1\_1",
language = "英语",
isbn = "9783319464534",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--18",
editor = "Bastian Leibe and Jiri Matas and Nicu Sebe and Max Welling",
booktitle = "Computer Vision - 14th European Conference, ECCV 2016, Proceedings",
}