Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
<|reference_start|>Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?: 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.<|reference_end|>
@article{kashkarov2024can,
    title={Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses
  for Super-Resolution?},
    author={Egor Kashkarov, Egor Chistov, Ivan Molodetskikh, Dmitriy Vatolin},
    journal={arXiv preprint arXiv:2405.20392},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20392},
    primaryClass={eess.IV cs.CV}
}