Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 18, Issue 4, Pages 549-562Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2016.2522639
Keywords
Eye tracking; high dynamic range (HDR); saliency map; visual attention
Categories
Funding
- NSERC [STPGP 447339-13]
- ICICS/TELUS People & Planet Friendly Home Initiative at UBC
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The human visual system (HVS) attempts to select salient areas to reduce cognitive processing efforts. Computational models of visual attention try to predict the most relevant and important areas of videos or images viewed by the human eye. Such models, in turn, can be applied to areas such as computer graphics, video coding, and quality assessment. Although several models have been proposed, only one of them is applicable to high dynamic range (HDR) image content, and no work has been done for HDR videos. Moreover, the main shortcoming of the existing models is that they cannot simulate the characteristics of HVS under the wide luminous range found in HDR content. This paper addresses these issues by presenting a computational approach to model the bottom-up visual saliency for HDR input by combining spatial and temporal visual features. An analysis of eye movement data affirms the effectiveness of the proposed model. Comparisons employing three well-known quantitative metrics show that the proposed model substantially improves predictions of visual attention for HDR content.
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