Image super-resolution (SR) has been widely investigated in recent years. However, it is challenging to fairly estimate the performance of various SR methods, as the lack of reliable and accurate criteria for the perceptual quality. Existing metrics concentrate on the specific kind of degradation without distinguishing the visual sensitive areas, which have no ability to describe the diverse SR degeneration situations in both low-level textural and high-level perceptual information. In this paper, we focus on the textural and perceptual degradation of SR images, and design a dual stream network to jointly explore the textural and perceptual information for quality assessment, dubbed TPNet. By mimicking the human vision system (HVS) that pays more attention to the significant image areas, we develop the spatial attention to make the visual sensitive information more distinguishable and utilize feature normalization (F-Norm) to boost the network representation. Experimental results show the TPNet predicts the visual quality score more accurate than other methods and demonstrates better consistency with the human's perspective. The source code will be available at \url{http://github.com/yuqing-liu-dut/NRIQA_SR}
翻译:近些年来,人们广泛调查了超分辨率图像(SR),然而,由于缺乏可靠和准确的感官质量标准,很难公平地估计各种SR方法的性能,因为缺乏可靠和准确的感官质量标准。现有的指标集中于特定类型的退化,而没有区分视觉敏感地区,因此无法描述低层次质素和高层次感官信息中不同的SR退化情况。在本文件中,我们侧重于SR图像的质谱和感官退化,并设计一个双流网络,共同探索质素和感官信息,以进行质量评估。通过模拟更多关注重要图像地区的人类视觉系统,我们发展空间注意力,使视觉敏感信息更加可辨别,并利用特征正常化(F-Norm)来增强网络的表达。实验结果显示,TPNet预测视觉质量评分比其他方法更准确,并显示与人类视角更一致。源代码将在以下网站提供:http://githhub.com/yququing-duuuuuu。