Learning preferential perceptual exposure for HDR displays

Bashford-Rogers, Thomas; Melo, Miguel; Marnerides, Demetris; Bessa, Maximino; Debattista, Kurt; Chalmers, Alan

Abstract

High Dynamic Range (HDR) displays are capable of displaying a wider dynamic range of values than conventional displays. As HDR content becomes more ubiquitous, the use of these displays is likely to accelerate. As HDR displays can present a wider range of values, traditional strategies for mapping HDR content to Low Dynamic Range (LDR) displays can be replaced with either directly displaying values, or using a simple shift mapping (exposure adjustment). The latter approach is especially important when considering ambient lighting, as content viewed in a dark environment may appear substantially different to a bright one. This work seeks to identify an exposure value which is suitable for displaying specific HDR content on an HDR display under a range of ambient lighting levels. Based on data captured with human participants, this work establishes user preferred exposure values for a variety of maximum display brightnesses, content and ambient lighting levels. These are then used to develop two models to predict the preferred exposure. The first is based on linear regression using straightforward image statistics which require minimal computation and memory to be computed, making this method suitable to be directly used in display hardware. The second is a model based on Convolutional Neural Networks (CNN) to learn image features which best predict exposure values. The CNN model generates better results than the first model at the cost of memory and computation time.

Citation

Bashford-Rogers, T., Melo, M., Marnerides, D., Bessa, M., Debattista, K., & Chalmers, A. (2019). Learning preferential perceptual exposure for HDR displays. IEEE Access, 7, 36800-36809. https://doi.org/10.1109/ACCESS.2019.2898910

• Journal Article Type: Article
• Acceptance Date: Feb 11, 2019
• Online Publication Date: Mar 25, 2019
• Publication Date: Mar 25, 2019
• Deposit Date: Apr 15, 2019
• Publicly Available Date: Apr 16, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 36800-36809
• DOI: https://doi.org/10.1109/ACCESS.2019.2898910
• Keywords: high dynamic range imaging, high dynamic range displays, deep learning, HDR
• Public URL: https://uwe-repository.worktribe.com/output/850127
• Publisher URL: http://dx.doi.org/10.1109/ACCESS.2019.2898910
• Additional Information: Additional Information : (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.