  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.
