Skip to Main content Skip to Navigation
Conference papers

Robust dynamic range computation for high dynamic range content

Abstract : 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.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download
Contributor : Frédéric Dufaux <>
Submitted on : Friday, January 10, 2020 - 12:07:53 PM
Last modification on : Wednesday, September 16, 2020 - 4:50:51 PM
Long-term archiving on: : Saturday, April 11, 2020 - 3:16:07 PM


Files produced by the author(s)


  • HAL Id : hal-01433779, version 1


Vedad Hulusic, Giuseppe Valenzise, Kurt Debattista, Frederic Dufaux. Robust dynamic range computation for high dynamic range content. Human Vision and Electronic Imaging Conference, IS&T International Symposium on Electronic Imaging (EI 2017), Jan 2017, Burlingame, United States. ⟨hal-01433779⟩