Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917679688843264 |
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| author | Kashkarov, Egor Chistov, Egor Molodetskikh, Ivan Vatolin, Dmitriy |
| author_facet | Kashkarov, Egor Chistov, Egor Molodetskikh, Ivan Vatolin, Dmitriy |
| contents | Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptual losses is often limited to LPIPS, a fullreference method. Even though deep no-reference image-qualityassessment methods are excellent at predicting human judgment, little research has examined their incorporation in loss functions. This paper investigates direct optimization of several video-superresolution models using no-reference image-quality-assessment methods as perceptual losses. Our experimental results show that straightforward optimization of these methods produce artifacts, but a special training procedure can mitigate them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution? Kashkarov, Egor Chistov, Egor Molodetskikh, Ivan Vatolin, Dmitriy Image and Video Processing Computer Vision and Pattern Recognition Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptual losses is often limited to LPIPS, a fullreference method. Even though deep no-reference image-qualityassessment methods are excellent at predicting human judgment, little research has examined their incorporation in loss functions. This paper investigates direct optimization of several video-superresolution models using no-reference image-quality-assessment methods as perceptual losses. Our experimental results show that straightforward optimization of these methods produce artifacts, but a special training procedure can mitigate them. |
| title | Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution? |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.20392 |