基于非局部梯度的图像质量评价算法

图像质量评价研究的目标在于模拟人类视觉系统对图像质量的感知过程，构建与主观评价结果尽可能一致的客观评价算法。现有的很多算法都是基于局部结构相似设计的，但人对图像的主观感知是高级的、语义的过程，而语义信息本质上是非局部的，因此图像质量评价应该考虑图像的非局部信息。该文突破了经典的基于局部信息的算法框架，提出一种基于非局部信息的框架，并在此框架内构建了一种基于非局部梯度的图像质量评价算法，该算法通过度量参考图像与失真图像的非局部梯度之间的相似性来预测图像质量。在公开测试数据库TID2008, LIVE, CSIQ上的数值实验结果表明，该算法能获得较好的评价效果。

Image Quality Assessment Algorithm Based on Non-local Gradient

The goal of Image Quality Assessment (IQA) research is to simulate the Human Visual System’s (HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.