Causal Disentanglement-Inspired Degradation Representation Learning for Full-Reference Image Quality Assessment 事件

Causal Disentanglement-Inspired Degradation Representation Learning for Full-Reference Image Quality Assessment arXiv:2604.21654v3 Announce Type: replace Abstract: Existing deep network-based full-reference image quality assessment (FR-IQA) models typically work by performing pairwise comparisons of deep features from the reference and distorted images. In this paper, we approach this problem from a different perspective and propose a novel FR-IQA paradigm based on causal inference and decouple