A Hybrid Conformer-based Full-Reference Image Quality Assessment for Perceptual Image Quality Analysis

Abstract

Image Quality Assessment is a trending area that leads to numerous applications in computer vision and systems. However, the methods in FR-IQA suffer from biases and content. The existing methods, like CNN, have limitations in a few aspects. This paper describes the Full Reference Image Quality assessment method through a Hybrid Conformer-based Full-Reference known as Frozen Vision Transformer backbone with distortion patterns and explicit modelling. The two challenges in IQA addressed in the proposed method are multiple degradations and content separation from distortion. The proposed ViT-B/16 is frozen to keep visual representations. The multiscale features are extracted and fed to parallel modules. The model learns a debiased SVD-guided subspace. This model is trained on KADID-10K and compared over three standard datasets, CSIQ, LIVE, and TID-2013, exhibits consistent performance with SRCC values 0.912, 0.924 and 0.908, respectively. The experimental study confirms that joint degradation and SVD alignment exhibit good performance.