  An important scenario for image quality assessment (IQA) is to evaluate image
restoration (IR) algorithms. The state-of-the-art approaches adopt a
full-reference paradigm that compares restored images with their corresponding
pristine-quality images. However, pristine-quality images are usually
unavailable in blind image restoration tasks and real-world scenarios. In this
paper, we propose a practical solution named degraded-reference IQA (DR-IQA),
which exploits the inputs of IR models, degraded images, as references.
Specifically, we extract reference information from degraded images by
distilling knowledge from pristine-quality images. The distillation is achieved
through learning a reference space, where various degraded images are
encouraged to share the same feature statistics with pristine-quality images.
And the reference space is optimized to capture deep image priors that are
useful for quality assessment. Note that pristine-quality images are only used
during training. Our work provides a powerful and differentiable metric for
blind IRs, especially for GAN-based methods. Extensive experiments show that
our results can even be close to the performance of full-reference settings.
