干噪声在视觉感知和图像质量测量中的作用
The Role of Stem Noise in Visual Perception and Image Quality Measurement
摘要：
本文认为参考自由失真和噪声图像的质量评估。具体而言，它考虑了可以在给定任何图像的情况下进行评估的干噪声的一阶和二阶统计。在图像质量评价（IQA）的研究领域中，干噪声被定义为自回归（AR）过程的输入，从中可以恢复低能量和去相关的图像。为了估计AR模型参数和相关联的干噪声能量，使用Yule-walker方程，使得伴随的自相关函数（ACF）系数可以被视为用于图像重建的模型参数。为了表征系统信号相关和信号无关失真，可以在图像上评估干噪声的均值和方差。至关重要的是，本文表明，这些统计数据有一个预测的有效性，人类的图像质量评级。此外，在某些类型的图像失真，干噪声统计显示出非常显着的相关性与图像质量的既定措施。
英文摘要：
This paper considers reference free quality assessment of distorted and noisy images. Specifically, it considers the first and second order statistics of stem noise that can be evaluated given any image. In the research field of Image quality Assessment (IQA), the stem noise is defined as the input of an Auto Regressive (AR) process, from which a low-energy and de-correlated version of the image can be recovered. To estimate the AR model parameters and associated stem noise energy, the Yule-walker equations are used such that the accompanying Auto Correlation Function (ACF) coefficients can be treated as model parameters for image reconstruction. To characterize systematic signal dependent and signal independent distortions, the mean and variance of stem noise can be evaluated over the image. Crucially, this paper shows that these statistics have a predictive validity in relation to human ratings of image quality. Furthermore, under certain kinds of image distortion, stem noise statistics show very significant correlations with established measures of image quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
