What Are the “Killer Apps” for HDR? Analysis of SDR Assets to Predict the Potential of HDR

Routhier, Pierre Hugues

doi:10.5594/jmi.2019.2896016

Cited by 1 publication

(1 citation statement)

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“…All the possibilities that the HDR format could offer, in terms of revenue and market growth for companies and in terms of improved user experience for the viewer, depend upon the existence of a fully functional HDR ecosystem. But we are currently a long way from such an ecosystem, as stated in recent reports from several international organizations and standardization bodies [29,70,75] as well as very recent publications from the motion picture and TV community [9,24,58,64,66,78]. This is due to open challenges happening at all stages of the production chain, from capture to display.…”

Section: Introductionmentioning

confidence: 99%

Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

Cyriac

Canham

Kane

et al. 2020

Multimed Tools Appl

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Many challenges that deal with processing of HDR material remain very much open for the film industry, whose extremely demanding quality standards are not met by existing automatic methods. Therefore, when dealing with HDR content, substantial work by very skilled technicians has to be carried out at every step of the movie production chain. Based on recent findings and models from vision science, we propose in this work effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition. These methods are automatic and real-time, and they have been both fine-tuned and validated by cinema professionals, with psychophysical tests demonstrating that the proposed algorithms outperform both the academic and industrial state-of-the-art. We believe these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally. Another contribution of our research is to highlight the limitations of existing image quality metrics when applied to the tone mapping problem, as none of them, including two state-of-the-art deep learning metrics for image perception, are able to predict the preferences of the observers.

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