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Volume: 29 | Article ID: art00019
Robust Dynamic Range Computation for High Dynamic Range Content
  DOI :  10.2352/ISSN.2470-1173.2017.14.HVEI-135  Published OnlineJanuary 2017

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.

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Vedad Hulusic, Giuseppe Valenzise, Kurt Debattista, Frédéric Dufaux, "Robust Dynamic Range Computation for High Dynamic Range Contentin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2017,  pp 151 - 155,

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