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When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents by Zhang, Weixia; Li, Dingquan; Zhai, Guangtao; Yang, Xiaokang; Ma, Kede is a scholarly article available to read on EtoBox.

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Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality, often achieving strong correlations with human perceptual scores on standard IQA benchmarks. Yet, limited efforts have been devoted to treating NR-IQA models as natural image priors for real-world image enhancement, and consequently comparing them from a perceptual optimization standpoint. In this work, we show -- for the first time -- that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement. This is achieved by performing gradient ascent in the diffusion latent space rather than in the raw pixel domain, leveraging a pretrained differentiable and bijective diffusion process. Likely, different NR-IQA models lead to different enhanced outputs, which in turn provides a new computational means of comparing them. Unlike conventional correlation-based measures, our comparison method offers complementary insights into the respective strengths and weaknesses of the competing NR-IQA models in perceptual optimization scenarios. Additionally, we aim to improve the best-performing NR-IQA model in diffusion latent MAP estima

Author

Zhang, Weixia; Li, Dingquan; Zhai, Guangtao; Yang, Xiaokang; Ma, Kede

Published

2024

Language

EN