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 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 tonemapped images. The first contribution of the paper is the setting of an experiment gathering human naturalness opinions on 1,900 SDR images mainly obtained from tone mapping operators. Based on the collected data, the second contribution of the paper 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.

The main goal of this paper is to study Image Aesthetic Assessment (IAA) indicating images as high or low aesthetic. The main contributions concern three points. Firstly, following the idea that photos in different categories (human, flower, animal, landscape, …) are taken with different photographic rules, image aesthetic should be evaluated in a different way for each image category. Large field images and close-up images are two typical categories of images with opposite photographic rules so we want to investigate the intuition that prior Large field/Close-up Image Classification (LCIC) might improve the performance of IAA. Secondly, when a viewer looks at a photo, some regions receive more attention than other regions. Those regions are defined as Regions Of Interest (ROI) and it might be worthy to identify those regions before IAA. The question “Is it worthy to extract some ROIs before IAA?” is considered by studying Region Of Interest Extraction (ROIE) before investigating IAA based on each feature set (global image features, ROI features and background features). Based on the answers, a new IAA model is proposed. The last point is about a comparison between the efficiency of handcrafted and learned features for the purpose of IAA.