Visual-Language Multi-Task Blind Image Quality Assessment With Local Quality Weighting

The objective of blind image quality assessment (BIQA) is to develop a model capable of automatically evaluating image quality without requiring any reference knowledge. While multi-task learning has been widely utilized in BIQA, it has predominantly remained unimodal. This paper delves into the Visual-Language multi-task BIQA model, where distortion knowledge can be captured through image-text contrastive learning. Specifically, Visual-Language auxiliary tasks targeting distortion type and quality level are introduced, respectively, where both positive and negative image-text pairs are constructed for the target distorted image. Subsequently, image-text correspondences are learned in the embedding space while simultaneously evaluating image quality. Notably, in the auxiliary task learning, the proposed method not only brings the image and its corresponding positive text prompt closer but also pushes away the image from its negative text prompts, thereby facilitating the extraction of pertinent distortion features. In the quality assessment task, a patch-wise strategy is employed during the training phase. Differing from conventional BIQA methods, a novel NSS-guided quality weighting is introduced to gauge the correlation between patch quality and global quality, thereby enabling precise quality prediction. Extensive experiments are conducted on six IQA datasets, and the experimental results verify the superiority of the proposed method.