Zewen Chen

IJCV

Hierarchical Curriculum Learning for No-Reference Image Quality Assessment

Juan Wang, Zewen Chen, Chunfeng Yuan et al. (Co-first author)

TL;DR: We develope a hierarchical curriculum learning (HCL) framework for NR-IQA by decomposing complex tasks into three levels: basic, intermediate, and professional to improve performance.

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This work addresses the problem of insufficient labeled data for no-reference image quality assessment (NR-IQA) with the help of pre-training techniques and external unsupervised data. We design a hierarchical curriculum learning (HCL) framework for NR-IQA, which leverages the external data to learn the prior knowledge about IQA widely and progressively. Specifically, as a closely-related task with NR-IQA, image restoration is used as the first curriculum to learn the image quality related knowledge (i.e., semantic and distortion information) on massive distorted-reference image pairs. Then multiple lightweight subnetworks are designed to learn human scoring rules on multiple available synthetic IQA datasets independently, and a cross-dataset quality assessment correlation (CQAC) module is proposed to fully explore the similarities and diversities of different scoring rules. Finally, the whole model is fine-tuned on the target authentic IQA dataset to fuse the learned knowledge and adapt to the target data distribution. The experimental results show that the designed pre-trained model can achieve good prediction accuracy and generalisation.