Skip to main content

Robust dynamic range computation for high dynamic range content.

Hulusic, V., Valenzise, G., Debattista, K. and Dufaux, F., 2017. Robust dynamic range computation for high dynamic range content. In: HVEI 2017 : IS&T Conference on Human Vision and Electronic Imaging, 29 January-2 February 2017, Burlingame, CA, USA, 151-155.

Full text available as:

hulusic2017robust.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.2352/ISSN.2470-1173.2017.14.HVEI-135


High dynamic range (HDR) imaging has become an important topic in both academic and industrial domains. Nevertheless, the concept of dynamic range (DR), which underpins HDR, and the way it is measured are still not clearly understood. The current approach to measure DR results in a poor correlation with perceptual scores (r ≈ 0.6). In this paper, we analyze the limitations of the existing DR measure, and propose several options to predict more accurately subjective DR judgments. Compared to the traditional DR estimates, the proposed measures show significant improvements in Spearman's and Pearson's correlations with subjective data (up to r ≈ 0.9). Despite their straightforward nature, these improvements are particularly evident in specific cases, where the scores obtained by using the classical measure have the highest error compared to the perceptual mean opinion score.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:30370
Deposited By: Symplectic RT2
Deposited On:19 Feb 2018 10:15
Last Modified:14 Mar 2022 14:09


Downloads per month over past year

More statistics for this item...
Repository Staff Only -