Back to Search
Start Over
No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking
- Source :
- AAAI
- Publication Year :
- 2019
- Publisher :
- Association for the Advancement of Artificial Intelligence (AAAI), 2019.
-
Abstract
- 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.
- Subjects :
- business.industry
Computer science
Image quality
media_common.quotation_subject
General Medicine
Machine learning
computer.software_genre
Task (project management)
Ranking (information retrieval)
Image (mathematics)
Ranking
Benchmark (computing)
Reinforcement learning
Quality (business)
Markov decision process
Artificial intelligence
business
computer
media_common
Subjects
Details
- ISSN :
- 23743468 and 21595399
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Proceedings of the AAAI Conference on Artificial Intelligence
- Accession number :
- edsair.doi...........e7c75245a0cddf0e97d01f140d083459