Estimating Visual Saliency for Omnidirectional HDR Images

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Author(s): Kenji Hara (Kyushu University, Japan)
Copyright: 2021
Pages: 24
Source title: Analyzing Future Applications of AI, Sensors, and Robotics in Society
Source Author(s)/Editor(s): Thomas Heinrich Musiolik (Berlin University of the Arts, Germany)and Adrian David Cheok (Professional University of Information and Management for Innovation, iUniversity, Tokyo, Japan)
DOI: 10.4018/978-1-7998-3499-1.ch015

Keywords: Artificial Intelligence / Human Aspects of Technology / Information Science Reference / Social Sciences & Humanities

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Abstract

A unified decomposition-and-integration-based framework is presented herein for the visual saliency estimation of omnidirectional high dynamic range (HDR) images, which allows straightforward reuse of existing saliency estimation method for typical images with narrow field-of-view and low dynamic range (LDR). First, the proposed method decomposes a given omnidirectional HDR image into multiple partially overlapping LDR images with quasi-uniform spatial resolution and without polar singularities, both spatially and in intensity using a spherical overset grid and a tone-mapping-based synthesis of imaginary multiexposure images. For each decomposed image, a standard saliency estimation method is then applied for typical images. Finally, the saliency map of each decomposed image is optimally integrated from the coordinate system of the overset grid and LDR back to the representation of the coordinate system and HDR of the original image. The proposed method is applied to actual omnidirectional HDR images and its effectiveness is demonstrated.

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Estimating Visual Saliency for Omnidirectional HDR Images

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