Tone mapping operators (TMOs) reproduce the high dynamic range (HDR) images on low dynamic range (LDR) consumer electronics devices such as monitors or printers. To accurately measure and compare the performance of different TMOs, this article proposes a tone-mapped images (TMIs) luminance partition model and corresponding quality measure. First, each tone-mapped (TM) image is segmented into highlight region (HR), dark region (DR) and midtone region (MR) based on luminance partition. Second, local entropies and contrast features are extracted in the HR and DR, and color-based features are captured in the MR. Meanwhile, the gray-level co-occurrence matrix (GLCM) and Canny operator are utilized to measure the microstructural distortions and halo effects, respectively. Finally, all extracted features are combined and trained together with subjective ratings to form a regression model using support vector regression (SVR). Experimental results show that the proposed method outperforms the state-of-the-art no-reference (NR) methods. Specifically, the spearman correlation coefficients (SRCC) values of our method reach 0.83 and 0.76 on the tone-mapped image database (TMID) and the ESPL-LIVE HDR database, respectively.

中文翻译：

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结合亮度分区和色彩指数的电子显示器色调映射图像质量评估

色调映射运算符 (TMO) 在低动态范围 (LDR) 消费电子设备（如显示器或打印机）上再现高动态范围 (HDR) 图像。为了准确测量和比较不同 TMO 的性能，本文提出了色调映射图像 (TMI) 亮度分区模型和相应的质量度量。首先，每个色调映射 (TM) 图像基于亮度分区被分割为高光区域 (HR)、暗区 (DR) 和中间色调区域 (MR)。其次，在 HR 和 DR 中提取局部熵和对比度特征，在 MR 中捕获基于颜色的特征。同时，利用灰度共生矩阵（GLCM）和 Canny 算子分别测量微观结构失真和光晕效应。最后，将所有提取的特征与主观评分结合起来训练，形成一个使用支持向量回归（SVR）的回归模型。实验结果表明，所提出的方法优于最先进的无参考（NR）方法。具体来说，我们的方法的斯皮尔曼相关系数（SRCC）值在色调映射图像数据库（TMID）和 ESPL-LIVE HDR 数据库上分别达到 0.83 和 0.76。