mlsanityData to 7 Oct 2026

Can No-reference features help in Full-reference image quality estimation?

arXiv 2203.00845 ↗First posted 2 Mar 2022eess.IV cs.AI cs.CVcited by 1

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Abstract (arXiv)

Development of perceptual image quality assessment (IQA) metrics has been of significant interest to computer vision community. The aim of these metrics is to model quality of an image as perceived by humans. Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction. However, pixelwise feature comparison may not be meaningful if distortion present in query image is severe. In this context, we explore utilization of no-reference features in Full-reference IQA task. Our model consists of both full-reference and no-reference branches. Full-reference branches use both distorted and reference images, whereas No-reference branch only uses distorted image. Our experiments show that use of no-reference features boosts performance of image quality assessment. Our model achieves higher SRCC and KRCC scores than a number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

23 of 34 references.

References · 23

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11 more
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  1. 2022NTIRE 2022 Challenge on Perceptual Image Quality Assessmentcited by 41