Papers › Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

18 Aug 2021ICCV 2021 10arXiv:2108.07948archive 2025-07-28

Heliang Zheng, Huan Yang, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo

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.

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Image Quality AssessmentImage RestorationKnowledge Distillation

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