In this paper, we investigate visual similarity for high dynamic range (HDR) images. We collect crowdsourcing data through a web-based experimental interface, in which the participants are asked to choose one of the two candidate images as being more similar to the query image. Triplets forming the query-and-candidates sets are obtained by random sampling from existing HDR data sets. Experimental control factors include choice of tone mapping operator (TMO), choice of distance metric, and choice of image feature. The image features that we experiment with are chosen from the features that are commonly used in the usual low dynamic range setting including features learned via Convolutional Neural Networks. The set of image features also includes combined features where the combination coefficients are estimated using logistic regression. We compute correlations between human judgments and quantitative features to understand how much each feature contributes to visual similarity. Combined features yield nearly 84% agreement with human judgments when applied on tone mapped images. Though we observed that using common features directly on raw or linearly scaled HDR images yield subpar correlation estimates compared to using them on tone mapped HDR images, we did not observe significant effect due to the choice of TMO on the estimates. As an application, we propose an improvement to style-based tone mapping for more correctly imparting desired styles to HDR images with different characteristics.

在本文中，我们研究了高动态范围 (HDR) 图像的视觉相似性。我们通过基于网络的实验界面收集众包数据，其中要求参与者选择与查询图像更相似的两个候选图像之一。形成查询和候选集的三元组是通过从现有 HDR 数据集中随机抽样获得的。实验控制因素包括色调映射算子 (TMO) 的选择、距离度量的选择和图像特征的选择。我们试验的图像特征选自通常用于低动态范围设置的特征，包括通过卷积神经网络学习的特征。图像特征集还包括组合特征，其中组合系数使用逻辑回归进行估计。我们计算人类判断和定量特征之间的相关性，以了解每个特征对视觉相似性的贡献程度。组合特征产生近 84 个当应用于色调映射图像时，与人类判断的一致性百分比。尽管我们观察到，与在色调映射的 HDR 图像上使用它们相比，直接在原始或线性缩放的 HDR 图像上使用共同特征会产生低于标准的相关性估计，但由于在估计上选择了 TMO，我们没有观察到显着影响。作为一个应用程序，我们提出了对基于风格的色调映射的改进，以便更正确地将所需风格赋予具有不同特征的 HDR 图像。