No-reference Image Quality Assessment Based on Multi-scale Feature Fusion
Image quality assessment is one of the basic technologies in the image field of image processing.The reference-free image quality assessment model does not require the use of reference images and has wider applicability.It has always been a hot research topic in the field of image quality assessment.This paper proposes a no-reference image quality assessment algorithm based on dense feature pyramid network and Swin Transformer.The dense feature pyramid network uses inter-layer residual connections,inter-layer dense connections and feature re-weighting strategies,and its top-down and bottom-up aggregation paths can aggregate multi-scale fea-tures more effectively.Swin Transformer introduces a window attention mechanism to better capture local and global information in ima-ges and reduce computational complexity.On the TID2013 data set,the SROCC and PLCC indicators of this algorithm are improved by 2.6％and 1.9％respectively compared with the original algorithm.On the KADID-10k data set,the SROCC and PLCC indicators are improved by 1.4％and 0.9％respectively,which improves the performance of the no-reference image quality assessment.
deep learningimage quality assessmentfeature pyramidmulti-scale feature fusionSwin Transformer
万方数据
维普
