Blind Image Quality Assessment of Natural Distorted Image Based on Generative Adversarial Networks

Most existing image quality assessment (IQA) methods focus on improving the performance of synthetic distorted images. Although these methods perform well on the synthetic distorted IQA database, once they are applied to the natural distorted database, the performance will severely decrease. In this work, we propose a blind image quality assessment based on generative adversarial network (BIQA-GAN) with its advantages of self-generating samples and self-feedback training to improve network performance. Three different BIQA-GAN models are designed according to the target domain of the generator. Comprehensive experiments on popular benchmarks show that our proposed method significantly outperforms the previous state-of-the-art methods for authentically distorted images, which also has good performances on synthetic distorted benchmarks.