DRL-IQA: Deep reinforcement learning for opinion-unaware blind image quality assessment
Ying Zefeng, Da Pan, Shi Ping · China Communications · 2025
Most blind image quality assessment (BIQA) methods require a large amount of time to collect human opinion scores as training labels, which limits their usability in practice. Thus, we present an opinion-unaware BIQA method based on deep reinforcement learning which is trained without subjective scores, named DRL-IQA. Inspired by the human visual perception process, our model is formulated as a quality reinforced agent, which consists of the dynamic distortion generation part and the quality perception part. By considering the image distortion degradation process as a sequential decision-making process, the dynamic distortion generation part can develop a strategy to add as many different distortions as possible to an image, which enriches the distortion space to alleviate overfitting. A reward function calculated from quality degradation after adding distortion is utilized to continuously optimize the strategy. Furthermore, the quality perception part can extract rich quality features from the quality degradation process without using subjective scores, and accurately predict the state values that represent the image quality. Experimental results reveal that our method achieves competitive quality prediction performance compared to other state-of-the-art BIQA methods.