High dynamic range (HDR) images give a strong disposition to capture all parts of natural scene information due to their wider brightness range than traditional low dynamic range (LDR) images. However, to visualize HDR images on common LDR displays, tone mapping operations (TMOs) are extra required, which inevitably lead to visual quality degradation, especially in the bright and dark regions. To evaluate the performance of different TMOs accurately, this paper proposes a blind tone-mapped image quality assessment method based on regional sparse response and aesthetics (RSRA-BTMI) by considering the influences of detail information and color on the human visual system. Specifically, for the detail loss in a tone-mapped image (TMI), multi-dictionaries are first designed for different brightness regions and whole TMI. Then regional sparse atoms aggregated by local entropy and global reconstruction residuals are presented to characterize the regional and global detail distortion in TMI, respectively. Besides, a few efficient aesthetic features are extracted to measure the color unnaturalness of TMI. Finally, all extracted features are linked with relevant subjective scores to conduct quality regression via random forest. Experimental results on the ESPL-LIVE HDR database demonstrate that the proposed RSRA-BTMI method is superior to the existing state-of-the-art blind TMI quality assessment methods.

中文翻译：

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基于区域稀疏响应和美学的盲色调映射图像质量评估

由于高动态范围 (HDR) 图像的亮度范围比传统的低动态范围 (LDR) 图像更广，因此它们可以很好地捕捉自然场景信息的所有部分。然而，为了在普通 LDR 显示器上可视化 HDR 图像，需要额外的色调映射操作 (TMO)，这不可避免地导致视觉质量下降，尤其是在亮区和暗区。为了准确评估不同TMOs的性能，本文考虑了细节信息和颜色对人类视觉系统的影响，提出了一种基于区域稀疏响应和美学的盲色调映射图像质量评估方法（RSRA-BTMI）。具体来说，对于色调映射图像（TMI）中的细节损失，首先针对不同的亮度区域和整个 TMI 设计多字典。然后提出由局部熵和全局重建残差聚合的区域稀疏原子，分别表征 TMI 中的区域和全局细节失真。此外，还提取了一些有效的美学特征来衡量 TMI 的颜色不自然度。最后，将所有提取的特征与相关的主观评分联系起来，通过随机森林进行质量回归。ESPL-LIVE HDR 数据库的实验结果表明，所提出的 RSRA-BTMI 方法优于现有的最先进的盲 TMI 质量评估方法。所有提取的特征都与相关的主观分数相关联，以通过随机森林进行质量回归。ESPL-LIVE HDR 数据库的实验结果表明，所提出的 RSRA-BTMI 方法优于现有的最先进的盲 TMI 质量评估方法。所有提取的特征都与相关的主观分数相关联，以通过随机森林进行质量回归。ESPL-LIVE HDR 数据库的实验结果表明，所提出的 RSRA-BTMI 方法优于现有的最先进的盲 TMI 质量评估方法。