Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICME52920.2022.9859721 | 2022 IEEE International Conference on Multimedia and Expo (ICME) |
Keywords | DocType | ISSN |
Full-Reference Image Quality Assess-ment,Image Similarity Metric,Convolutional Neural Net-work,Multi-Layer Perceptron | Conference | 1945-7871 |
ISBN | Citations | PageRank |
978-1-6654-8564-7 | 0 | 0.34 |
References | Authors | |
16 | 5 | |