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Study of naturalness in tone-mapped images

Abstract : 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 tone-mapped 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.
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Submitted on : Wednesday, May 13, 2020 - 11:33:53 AM
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Quyet Tien Le, Patricia Ladret, Huu-Tuan Nguyen, Alice Caplier. Study of naturalness in tone-mapped images. Computer Vision and Image Understanding, Elsevier, 2020, 196, pp.102971. ⟨10.1016/j.cviu.2020.102971⟩. ⟨hal-02568771⟩

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