In order to show high dynamic range (HDR) images by traditional displays, various tone-mapping operators have been designed to convert HDR images into low dynamic range (LDR) images recently. However, how to estimate the visual quality of LDR images effectively is still challenging. In this paper, we propose a novel blind quality assessment method for tone-mapped images with the consideration of naturalness and the perceptual characteristics of human visual system (HVS). First, we design parametric models that describe characteristics of chromatic information in tone-mapped images and extract quality-aware features based on global statistics model to characterize the naturalness of tone-mapped images. Second, motivated by perceptual characteristics that the HVS is highly adaptive to the image texture, we employ local texture features to capture the quality degradation of tone-mapped images. Support vector regression (SVR) is used to train the quality prediction model from features to human ratings. Experimental results indicate that the proposed metric can get better performance in predicting the visual quality of tone-mapped images than the state-of-the-art methods.

为了通过传统显示器显示高动态范围（HDR）图像，最近设计了各种色调映射运算符以将HDR图像转换为低动态范围（LDR）图像。但是，如何有效地估计LDR图像的视觉质量仍然是一个挑战。在本文中，我们提出了一种新的针对色调映射图像的盲质量评估方法，该方法考虑了自然性和人类视觉系统（HVS）的感知特性。首先，我们设计参数模型，描述色调映射图像中色度信息的特征，并基于全局统计模型提取质量感知特征，以表征色调映射图像的自然性。其次，受感知特性的启发，HVS非常适合图像纹理，我们采用局部纹理特征来捕获色调映射图像的质量下降。支持向量回归（SVR）用于训练从特征到人类评级的质量预测模型。实验结果表明，与最新方法相比，所提出的度量在预测色调映射图像的视觉质量方面可以获得更好的性能。