In this paper, we propose a graph-represented image distribution similarity (GRIDS) index for full-reference (FR) image quality assessment (IQA), which can measure the perceptual distance between distorted and reference images by assessing the disparities between their distribution patterns under a graph-based representation. First, we transform the input image into a graph-based representation, which is proven to be a versatile and effective choice for capturing visual perception features. This is achieved through the automatic generation of a vision graph from the given image content, leading to holistic perceptual associations for irregular image regions. Second, to reflect the perceived image distribution, we decompose the undirected graph into cliques and then calculate the product of the potential functions for the cliques to obtain the joint probability distribution of the undirected graph. Finally, we compare the distances between the graph feature distributions of the distorted and reference images at different stages; thus, we combine the distortion distribution measurements derived from different graph model depths to determine the perceived quality of the distorted images. The empirical results obtained from an extensive array of experiments underscore the competitive nature of our proposed method, which achieves performance on par with that of the state-of-the-art methods, demonstrating its exceptional predictive accuracy and ability to maintain consistent and monotonic behaviour in image quality prediction tasks. The source code is publicly available at the following website https://github.com/Land5cape/GRIDS .

中文翻译：

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用于全参考图像质量评估的图表示的分布相似性指数

在本文中，我们提出了一种用于全参考（FR）图像质量评估（IQA）的图表示的图像分布相似性（GRIDS）指数，该指数可以通过评估失真图像和参考图像分布模式之间的差异来测量失真图像和参考图像之间的感知距离基于图形的表示。首先，我们将输入图像转换为基于图形的表示，这被证明是捕获视觉感知特征的通用且有效的选择。这是通过从给定图像内容自动生成视觉图来实现的，从而导致不规则图像区域的整体感知关联。其次，为了反映感知图像的分布，我们将无向图分解为派，然后计算派的势函数的乘积，以获得无向图的联合概率分布。最后，我们比较不同阶段失真图像和参考图像的图特征分布之间的距离；因此，我们结合从不同图模型深度导出的失真分布测量值来确定失真图像的感知质量。从大量实验中获得的实证结果强调了我们提出的方法的竞争性质，该方法的性能与最先进的方法相当，证明了其卓越的预测准确性以及保持一致和单调行为的能力在图像质量预测任务中。源代码可在以下网站公开获取https://github.com/Land5cape/GRIDS 。