Image quality assessment based on meta-learning and distortion perception
The cost of obtaining subjective quality scores for images is often high,image quality assessment(IQA)models commonly face the challenge of insufficient training samples.Additionally,the type of distortion has a significant impact on the perceived visual quality of images.In light of these considerations,this paper proposes an image quality assessment method that combines meta-learning and distortion perception.Firstly,meta-learning is employed to simulate the human learning process,enabling the rapid acquisition of prior knowledge about known distortion types.This knowledge guides the subsequent ResNet-50 network in effectively integrating multi-scale features.The introduction of a distortion perception module captures comprehensive distortion information,establishing a unified quality assessment framework.Experimental results on synthetic and real distortion datasets such as LIVE and KonIQ-10k indicate that the proposed model under small sample conditions can enhance the generalization performance across different distortion types.In comparison with the existing advanced methods,the model in this paper achieves 1.02%and 1.85%improvement in PLCC and SROCC evaluation indexes compared with the second-best method.In comparison with mainstream IQA models,the evaluation accuracy shows competitive performance.
万方数据
维普
