Comparison of No-Reference Image Quality Models via MAP Estimation in Diffusion Latents

arXiv:2403.06406v1 Announce Type: new
Abstract: Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify the perceived image quality, with high correlations between model predictions and human perceptual scores on fixed test sets. However, little progress has been made in comparing NR-IQA models from a perceptual optimization perspective. Here, for the first time, we demonstrate that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement. This is achieved by taking the gradients in …

abstract arxiv assessment comparison correlations cs.cv diffusion however human image map optimization perspective predictions progress quality reference test type via