@inproceedings{6d977ba9f66d4a6fb62e723f90ef461e,
title = "Learning qualitative and quantitative image quality assessment",
abstract = "Quantitative human evaluations give a much finer description while qualitative human evaluations are more stable, consistent and can be much easier to be obtained. Quantitative assessments have been widely explored, while the interaction between qualitative and quantitative evaluations has barely been exploited. A deep convolutional neural network with multi-task learning framework was utilized to perform quantitative evaluations and qualitative evaluations at the same time. The supervision of qualitative evaluations could help the model overcome the inconsistency existed in quantitative evaluations. Further, multi-task learning gives more information to facilitate the learning of discriminative features to describe image quality. As shown in the experiments, referring to qualitative evaluations has boosted the performance of quantitative assessments and the state of art performance has been achieved.",
keywords = "Deep convolutional neural network, Multi-task learning, Qualitative, Quantitative",
author = "Yudong Liang and Jinjun Wang and Ze Yang and Yihong Gong and Nanning Zheng",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th Pacific-Rim Conference on Multimedia, PCM 2016 ; Conference date: 15-09-2016 Through 16-09-2016",
year = "2016",
doi = "10.1007/978-3-319-48896-7\_41",
language = "英语",
isbn = "9783319488950",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "417--427",
editor = "Enqing Chen and Yun Tie and Yihong Gong",
booktitle = "Advances in Multimedia Information Processing {\textendash} 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings",
}