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Languages: English
Types: Article
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ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_COMPUTERGRAPHICS, GeneralLiterature_MISCELLANEOUS
Recent advances in high dynamic range (HDR) capturing and display technologies attracted a lot of interest to HDR imaging. Many issues that are considered as being resolved for conventional low dynamic range (LDR) images pose new challenges in HDR context. One of such issues is human visual attention, which has important applications in image and video compression, camera and displays manufacturing, artistic content creation, and advertisement. However, the impact of HDR imaging on visual attention and on the performance of saliency models is not well understood. Therefore, in this paper, we address this problem by creating a publicly available dataset of 46 HDR and corresponding LDR images with varying regions of interests, scenes, and dynamic range. We conducted eye tracking experiments and obtained fixation density maps, which demonstrate a significant difference in the way HDR and LDR capture attention of the observers.
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