Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?

• URL: http://arxiv.org/abs/2405.20392v1
• Date: Thu, 30 May 2024 18:04:58 GMT
• Title: Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
• Authors: Egor Kashkarov, Egor Chistov, Ivan Molodetskikh, Dmitriy Vatolin,
• Abstract summary: Perceptual losses play an important role in constructing deep-neural-network-based methods. This paper investigates direct optimization of several video-superresolution models using no-reference image-quality-assessment methods as perceptual losses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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