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- W4293519120 abstract "Full-reference image quality assessment aims to predict the perceptual quality of a distorted image based on its similarity to the pristine reference. In this paper, we propose a robust image similarity metric by fully exploring the representation power of deep learning-based features. A convolutional neu-ral network (CNN) is adopted to extract deep features from multiple scales. We show that such CNN features that con-tain multi -scale visual information are comprehensive and ro-bust enough for quality assessment. We further propose a quality-oriented feature regression (QOFR) module based on the multi-layer perceptron architecture. The QOFR module can efficiently integrate hierarchy CNN features and generate the final quality score. Extensive experiments on the bench-mark datasets demonstrate that our method achieves state-of-the-art performance with outstanding robustness and general-ization ability." @default.
- W4293519120 created "2022-08-30" @default.
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- W4293519120 date "2022-07-18" @default.
- W4293519120 modified "2023-10-16" @default.
- W4293519120 title "Quality-Oriented Feature Regression for Robust Image Similarity Metric" @default.
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- W4293519120 doi "https://doi.org/10.1109/icme52920.2022.9859721" @default.
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