TTL-IQA: Transitive Transfer Learning Based No-Reference Image Quality Assessment
Xiaohan Yang, Fan Li, Hantao Liu
Abstract
Image quality assessment (IQA) based on deep learning faces the overfitting problem due to limited training samples available in existing IQA databases. Transfer learning is a plausible solution to the problem, in which the shared features derived from the large-scale Imagenet source domain could be transferred from the original recognition task to the intended IQA task. However, the Imagenet source domain and the IQA target domain as well as their corresponding tasks are not directly related. In this paper, we propose a new transitive transfer learning method for no-reference image quality assessment (TTL-IQA). First, the architecture of the multi-domain transitive transfer learning for IQA is developed to transfer the Imagenet source domain to the auxiliary domain, and then to the IQA target domain. Second, the auxiliary domain and the auxiliary task are constructed by a new generative adversarial network based on distortion translation (DT-GAN). Furthermore, a TTL network of the semantic features transfer (SFTnet) is proposed to optimize the shared features for the TTL-IQA. Experiments are conducted to evaluate the performance of the proposed method on various IQA databases, including the LIVE, TID2013, CSIQ, LIVE multiply distorted and LIVE challenge. The results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, our proposed method demonstrates a strong generalization ability.