【摘要】 为了克服针对特定失真类型的局限性以及避免有监督的学习过程,通过视觉注意模型和边缘信息来构造特征池,提出了一种基于特征池的不区分失真类型以及无监督的无参考图像质量评价算法。该算法不针对特定失真类型,对各种失真类型的图像都能做出较好的评价,从这个角度来说,是一种通用型算法。此外,该算法不需要主观分值的训练,因而又是一种真正的无监督的质量评价算法。而且,在提取空域特征时,考虑了人类的视觉感知特性,认为感兴趣区域以及边缘块会显著地影响人们对图像质量的评价。实验结果表明,该算法性能与人们的主观感知具有较好的一致性。更多还原

【Abstract】 To overcome the shortcomings of distortion-specific algorithms and avoid supervised training,a novel no-reference image quality assessment method based on feature pool constructed by visual attention model and edge information is proposed,which is non-distortion-specific and unsupervised. This approach which doesn’t limit itself to one or more specific types of distortions can make a good evaluation to all sorts of distorted images,so it is a general-purpose algorithm from this point of view. Besides,the proposed method doesn’t have to train with subjective scores,so it is also a truly unsupervised image assessment algorithm. Furthermore,human visual perception characteristics are taken into account when spatial features are extrated from raw-image-patches and assume that regions of interest and edge blocks could affect image quality dramatically. Experimental results show that algorithm performance has good agreements with humans subjective perception.更多还原