School of Computer Engineering, Nanyang Technological University, Singapore.
IEEE Trans Image Process. 2012 Apr;21(4):1500-12. doi: 10.1109/TIP.2011.2175935. Epub 2011 Nov 15.
In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.
本文提出了一种新的图像质量评估 (IQA) 方案，重点关注梯度相似性。梯度传递重要的视觉信息，对于场景理解至关重要。利用这些信息，可以有效地捕捉结构和对比度的变化。因此，我们使用梯度相似性来衡量图像对比度和结构的变化。除了结构/对比度的变化，图像质量还受到亮度变化的影响，为了进行全面和更稳健的 IQA，必须考虑到这些变化。因此，所提出的方案考虑了亮度和对比度-结构的变化，以有效地评估图像质量。此外，所提出的方案旨在更紧密地遵循掩蔽效应和可见性阈值，即当掩蔽和掩蔽信号都较小时，所提出的方案更有效地处理这种情况。最后，通过自适应方法将亮度和对比度-结构变化的影响进行集成，以获得整体图像质量评分。通过与相关最先进方案的比较，使用六个公开的主观评分数据库（包含各种图像和失真类型）进行的广泛实验验证了所提出方案的有效性、鲁棒性和效率。
