Nowadays, images can be obtained in various ways such as capturing photos in single-exposure mode, applying Multiple Exposure Fusion algorithms to generate an image from multiple shoots of the same scene, mapping High Dynamic Range (HDR) images to Standard Dynamic Range (SDR) images, converting raw formats to displayable formats, or applying post-processing techniques to enhance image quality, aesthetic quality,…When looking at some photos, one might have a feeling of unnaturalness. This paper deals with the problem of developing a model firstly to estimate if an image looks natural or not to humans and the second purpose is to try to understand how the unnaturalness feeling is induced by a photo: Are there specific unnaturalness clues or is unnaturalness a general feeling when looking at a photo? The study focuses on SDR images, especially on tone-mapped images. The first contribution of the paper is the setting of an experiment gathering human naturalness opinions on 1900 SDR images mainly obtained from tone mapping operators. Based on the collected data, the second contribution is to study the efficiency of different feature types including handcrafted features and learned features for image naturalness analysis. A binary classification model is then developed based on the determined features to classify if an image looks natural or unnatural.

如今，可以通过多种方式获得图像，例如以单次曝光模式拍摄照片，应用多次曝光融合算法从同一场景的多次拍摄生成图像，将高动态范围（HDR）图像映射到标准动态范围（SDR） ）图像，将原始格式转换为可显示的格式，或应用后处理技术来提高图像质量，美学质量，……看一些照片时，可能会有不自然的感觉。本文研究的问题是，首先要开发模型以估计图像对人类看起来是否自然，其次是要试图了解照片是如何引起不自然感觉的：是否存在特定的不自然线索或不自然现象？看照片时的一般感觉？这项研究着重于SDR图像，特别是在色调映射的图像上。本文的第一个贡献是进行了一项实验，该实验收集了主要从色调映射运算符获得的关于1900个SDR图像的人类自然意见。基于收集的数据，第二个贡献是研究不同特征类型的效率，包括手工特征和用于图像自然度分析的学习特征。然后基于确定的特征开发二进制分类模型以对图像看起来是自然的还是不自然的进行分类。第二个贡献是研究不同特征类型的效率，包括手工特征和学习的特征，以进行图像自然度分析。然后基于确定的特征开发二进制分类模型以对图像看起来是自然的还是不自然的进行分类。第二个贡献是研究不同特征类型的效率，包括手工特征和学习的特征，以进行图像自然度分析。然后基于确定的特征开发二进制分类模型以对图像看起来是自然的还是不自然的进行分类。