Abstract
The deep learning revolution has strongly impacted low-level image processing tasks such as style/domain transfer, enhancement/restoration, and visual quality assessments. Despite often being treated separately, the aforementioned tasks share a common theme of understanding, editing, or enhancing the appearance of input images without modifying the underlying content. We leverage this observation to develop a novel disentangled representation learning method that decomposes inputs into content and appearance features. The model is trained in a self-supervised manner and we use the learned features to develop a new quality prediction model named DisQUE. We demonstrate through extensive evaluations that DisQUE achieves state-of-the-art accuracy across quality prediction tasks and distortion types. Moreover, we demonstrate that the same features may also be used for image processing tasks such as HDR tone mapping, where the desired output characteristics may be tuned using example input-output pairs.
Abstract (translated)
深度学习革命对诸如风格/领域转移、增强/修复和视觉质量评估等低级图像处理任务产生了强烈影响。尽管这些任务通常被单独处理，但前述任务都 share a common theme of understanding、editing或增强输入图像的视觉效果，而不会修改底层内容。我们利用这个观察结果开发了一种新颖的解耦表示学习方法，将输入分解为内容和外观特征。该模型以自监督的方式进行训练，并使用学习到的特征开发了一个名为DisQUE的新质量预测模型。我们在广泛的评估中证明了DisQUE在质量预测任务和失真类型上的最先进准确度。此外，我们还证明了相同特征还可以用于图像处理任务，如 HDR 色调映射，其中所需的输出特性可以通过使用示例输入-输出对进行调整。
URL
https://arxiv.org/abs/2404.13484
