What Hinders Perceptual Quality of PSNR-oriented Methods?
| Autor: | Xu, Tianshuo, Mi, Peng, Zheng, Xiawu, Li, Lijiang, Chao, Fei, Jiang, Guannan, Zhang, Wei, Zhou, Yiyi, Ji, Rongrong |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
I.4.4 Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) FOS: Electrical engineering electronic engineering information engineering Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing |
| Popis: | In this paper, we discover two factors that inhibit POMs from achieving high perceptual quality: 1) center-oriented optimization (COO) problem and 2) model's low-frequency tendency. First, POMs tend to generate an SR image whose position in the feature space is closest to the distribution center of all potential high-resolution (HR) images, resulting in such POMs losing high-frequency details. Second, $90\%$ area of an image consists of low-frequency signals; in contrast, human perception relies on an image's high-frequency details. However, POMs apply the same calculation to process different-frequency areas, so that POMs tend to restore the low-frequency regions. Based on these two factors, we propose a Detail Enhanced Contrastive Loss (DECLoss), by combining a high-frequency enhancement module and spatial contrastive learning module, to reduce the influence of the COO problem and low-Frequency tendency. Experimental results show the efficiency and effectiveness when applying DECLoss on several regular SR models. E.g, in EDSR, our proposed method achieves 3.60$\times$ faster learning speed compared to a GAN-based method with a subtle degradation in visual quality. In addition, our final results show that an SR network equipped with our DECLoss generates more realistic and visually pleasing textures compared to state-of-the-art methods. %The source code of the proposed method is included in the supplementary material and will be made publicly available in the future. 10 pages,7 figures |
| Databáze: | OpenAIRE |
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