基于元原型网络的无参考图像质量评价OA北大核心CSTPCD

The model based on deep learning requires a large amount of annotated data,so it is subjected to fine-tune on the pre-training model,which leads to insufficient generalization when facing new tasks.In view of this,a no-reference IQA(NR-IQA)algorithm based on meta prototype network is proposed.The meta prototype network is used to extract meta-knowledge in relevant tasks to form a quality prior model,which can quickly fulfill generalization when facing unknown tasks.The meta-learning method is used to obtain shared prior knowledge of various distortions on different distortion datasets to form the quality prior model.In order to better capture shared prior knowledge of various distortion scenarios,the meta prototype units are used to reconstruct image features to obtain richer prior knowledge,which facilitates the subsequent process of quality score prediction.The quality prior model is fine-tuned on the object task to construct the quality model.Experimental results on the three databases of CID2013,LIVE challenge and KonIQ-10K show that the proposed method has better performance.