@article{Gu_Meng_Da_Xiang_Pan_2019, title={No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4847}, DOI={10.1609/aaai.v33i01.33018336}, abstractNote={<p>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.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Gu, Jie and Meng, Gaofeng and Da, Cheng and Xiang, Shiming and Pan, Chunhong}, year={2019}, month={Jul.}, pages={8336-8343} }