基于多特征相似性融合的半参考图像质量评价

Reduced-reference image quality assessment based on multi-feature similarity fusion

提出了一种基于多特征相似性融合的半参考图像质量评价方法.在多个特征域,该算法采用不同的策略,分别评估了原始与待测图像之间的视觉相似性,并利用特征融合模块将上述特征相似性度量合并为图像整体质量指标,即MFSIQ(Multi-Feature Similarity based Image Quality).为验证所提出算法的性能,本研究分别在三个主流数据库上,将所提出算法与主流的全参考以及无参考算法进行了性能比较.实验结果表明,MFSIQ的性能优于两种主流半参考算法和全参考算法PSNR,甚至可以媲美人们所熟知的全参考算法SSIM.

In this paper,an image similarity metric based on multi-feature similarity fusion is proposed for reduced-reference image quality assessment(IQA).After estimating the visual similarity between reference and test images in multiple feature domains(grid color moment,Gabor wavelet texture,LBP and GIST)and using different comparison strategies,the feature pooling module combines the resulting feature similarity measures into an image quality indicator,namely the Multi-Feature Similarity based Image Quality index(MFSIQ).To validate the performance of MFSIQ,it is evaluated on three popular image databases(LIVE,IVC and MICT),against existing IQA algorithms of both reduced-reference and full-reference.The experiments demonstrate that the performance of MFSIQ is better than two reduced-reference IQA algorithms and full reference PSNR,and even comparable with the well-known full reference SSIM.