No-reference Image Quality Assessment with Reinforcement Recursive List-wise Ranking
文献类型:会议论文
作者 | Jie, Gu1,2![]() ![]() ![]() ![]() |
出版日期 | 2019 |
会议日期 | 2019.01.27-2019.02.01 |
会议地点 | 美国夏威夷 |
英文摘要 | Opinion-unaware no-reference image quality assessment (NR-IQA) methods have received many interests recently because they do not require images with subjective scores for training. Unfortunately, it is a challenging task, and thus far no opinion-unaware methods have shown consistently better performance than the opinion-aware ones. In this paper, we propose an effective opinion-unaware NR-IQA method based on reinforcement recursive list-wise ranking. We formulate the NR-IQA as a recursive list-wise ranking problem which aims to optimize the whole quality ordering directly. During training, the recursive ranking process can be modeled as a Markov decision process (MDP). The ranking list of images can be constructed by taking a sequence of actions, and each of them refers to selecting an image for a specific position of the ranking list. Reinforcement learning is adopted to train the model parameters, in which no ground-truth quality scores or ranking lists are necessary for learning. Experimental results demonstrate the superior performance of our approach compared with existing opinion-unaware NR-IQA methods. Furthermore, our approach can compete with the most effective opinion-aware methods. It improves the state-of-the-art by over 2% on the CSIQ benchmark and outperforms most compared opinion-aware models on TID2013. |
源URL | [http://ir.ia.ac.cn/handle/173211/23710] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Jie, Gu,Gaofeng, Meng,Cheng, Da,et al. No-reference Image Quality Assessment with Reinforcement Recursive List-wise Ranking[C]. 见:. 美国夏威夷. 2019.01.27-2019.02.01. |
入库方式: OAI收割
来源:自动化研究所
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