Blind Image Quality Assessment via Cross-View Consistency

Image quality assessment (IQA) is very important for both end-users and service-providers since a high-quality image can significantly improve the user's quality of experience (QoE). Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, a BIQA model is proposed that consists of a desirable self-supervised feature learning approach to mitigate the data shortage problem and learn comprehensive feature representations, and a self-attention-based feature fusion module to introduce self-attention mechanism. We develop the image quality assessment model under the framework of contrastive learning with multi views. Since human visual system perceives signals through multiple channels, the most important visual information should exist among all views of the channels. So we design the cross-view consistent information mining (CVC-IM) module to extract compact mutual information between different views. Color information and pseudo-reference image (PRI) of different distortion types are employed to formulate rich feature embeddings and preserve the quality-aware fidelity of learned representations. We employ the Transformer as the self-attention-based architecture to integrate feature embeddings. Extensive experiments show that our model achieves remarkable image quality assessment results on in-the-wild IQA datasets.