一种基于感知相关均方误差的图像质量度量方法。

A perceptually relevant MSE-based image quality metric.

出版信息

IEEE Trans Image Process. 2013 Nov;22(11):4447-59. doi: 10.1109/TIP.2013.2273671.

DOI:10.1109/TIP.2013.2273671

Abstract

Image quality metrics (IQMs), such as the mean squared error (MSE) and the structural similarity index (SSIM), are quantitative measures to approximate perceived visual quality. In this paper, through analyzing the relationship between the MSE and the SSIM under an additive noise distortion model, we propose a perceptually relevant MSE-based IQM, MSE-SSIM, which is expressed in terms of the variance of the source image and the MSE between the source and distorted images. Evaluations on three publicly available databases (LIVE, CSIQ, and TID2008) show that the proposed metric, despite requiring less computation, compares favourably in performance to several existing IQMs. In addition, due to its simplicity, MSE-SSIM is amenable for the use in a wide range of image and video tasks that involve solving an optimization problem. As an example, MSE-SSIM is used as the objective function in designing a Wiener filter that aims at optimizing the perceptual visual quality of the output. Experimental results show that the images filtered with a MSE-SSIM-optimal Wiener filter have better visual quality than those filtered with a MSE-optimal Wiener filter.

摘要

图像质量指标（IQM），如均方误差（MSE）和结构相似性指数（SSIM），是一种用于近似感知视觉质量的定量度量方法。在本文中，通过分析在加性噪声失真模型下 MSE 和 SSIM 之间的关系，我们提出了一种基于感知相关的 MSE 图像质量指标 MSE-SSIM，它是用源图像的方差和源图像与失真图像之间的 MSE 表示的。在三个公开可用的数据库（LIVE、CSIQ 和 TID2008）上的评估表明，尽管所提出的度量方法需要的计算量更少，但与几个现有的 IQM 相比，它的性能表现更好。此外，由于其简单性，MSE-SSIM 适用于涉及解决优化问题的广泛的图像和视频任务。例如，MSE-SSIM 被用作设计旨在优化输出感知视觉质量的维纳滤波器的目标函数。实验结果表明，用 MSE-SSIM 优化的维纳滤波器滤波后的图像比用 MSE 优化的维纳滤波器滤波后的图像具有更好的视觉质量。