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Eurographics Symposium on Rendering (EGSR 2021)

 
Modeling Surround-aware Contrast Sensitivity
 
  Shinyoung Yi   Daniel S. Jeon Ana Serrano§  
  Se-Yoon Jeong* Hui-Yong Kim* Diego Gutierrez§   Min H. Kim  
           
  KAIST § Universidad de Zaragoza, I3A * ETRI    
 
 
  We compare a reference HDR video frame and a lossy compressed frame using our surround-aware CSF model. We compress the original video by three orders of magnitude without perceivable artifacts. Refer to the supplemental video for more results.  
     
     
   
  Supplemental video
   
  Abstract
   
 

Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state-of-the-art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surroundaware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone-mapping, and improved accuracy in visual difference prediction.

   
  BibTeX
 
@InProceedings{Yi_2021_EGSR,
author = {Shinyoung Yi and Daniel S. Jeon and Ana Serrano and 
  Se-Yoon Jeong and Hui-Yong Kim and Diego Gutierrez and Min H. Kim},
title = {Modeling Surround-aware Contrast Sensitivity},
booktitle = {Proc. Eurographics Symposium on Rendering (EGSR) 2021},
month = {June},
year = {2021}
}
   
   
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EGSR preprint:
PDF (9.1MB)
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Supplemental
document:
PDF (681KB)
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Presentation
slides:
PDF (9.1MB)
www KAIST S-CSF dataset (TBA)
www GitHub (TBA)
website

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