Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 31, Issue -, Pages 6139-6151Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2022.3205770
Keywords
Feature extraction; Distortion; Training; Task analysis; Distortion measurement; Predictive models; Image quality; Image quality assessment; no-reference; mutual learning; pseudo-reference feature
Funding
- National Natural Science Foundation of China [62022002]
- Shenzhen Virtual University Park
- Science Technology and Innovation Committee of Shenzhen Municipality [2021Szvup128]
- Hong Kong Research Grants Council General Research Fund (GRF) [11203220]
Ask authors/readers for more resources
In this paper, a no-reference image quality assessment method based on feature level pseudo-reference hallucination is proposed. The method utilizes perceptually meaningful features to characterize visual quality and leverages natural image statistical behaviors for accurate predictions. Experimental results demonstrate the effectiveness and high generalization capability of the proposed method on multiple databases.
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The proposed quality assessment framework is rooted in the view that the perceptually meaningful features could be well exploited to characterize the visual quality, and the natural image statistical behaviors are exploited in an effort to deliver the accurate predictions. Herein, the PR features from the distorted images are learned by a mutual learning scheme with the pristine reference as the supervision, and the discriminative characteristics of PR features are further ensured with the triplet constraints. Given a distorted image for quality inference, the feature level disentanglement is performed with an invertible neural layer for final quality prediction, leading to the PR and the corresponding distortion features for comparison. The effectiveness of our proposed method is demonstrated on four popular IQA databases, and superior performance on cross-database evaluation also reveals the high generalization capability of our method. The implementation of our method is publicly available on https://github.com/Baoliang93/FPR.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available