Multi-Scale Dense Description for Blind Image Quality Assessment

Blind image quality assessment (BIQA) aims to predict perceptual quality without access to reference images. However, discerning both image content and complex distortions within a unified model poses key challenges. To address this, we propose a single-stage BIQA framework termed multi-scale dense description (MSDD). The key innovation is a dense quality description (DQD) module that matches tailored distortion portrayals to each spatial location using a learned aggregation. This forms fine-grained alignments between contents and distortions. The content and distortion fusion (CDF) module then fuses them via a improved squash function. Finally, the multi-scale quality prediction (MSQP) module recursively combines evidence across stages through cross-scale integration. Extensive experiments show that MSDD achieves state-of-the-art performance and robustness on diverse distortions and contents.