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Time-dependent visual adaptation for fast realistic image display
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Source International Conference on Computer Graphics and Interactive Techniques archive
Proceedings of the 27th annual conference on Computer graphics and interactive techniques table of contents
Pages: 47 - 54  
Year of Publication: 2000
ISBN:1-58113-208-5
Authors
Sumanta N. Pattanaik  Program of Computer Graphics, Cornell University
Jack Tumblin  Program of Computer Graphics, Cornell University
Hector Yee  Program of Computer Graphics, Cornell University
Donald P. Greenberg  Program of Computer Graphics, Cornell University
Sponsor
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM Press/Addison-Wesley Publishing Co.  New York, NY, USA
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Downloads (6 Weeks): 6,   Downloads (12 Months): 50,   Citation Count: 28
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ABSTRACT

Human vision takes time to adapt to large changes in scene intensity, and these transient adjustments have a profound effect on visual appearance. This paper offers a new operator to include these appearance changes in animations or interactive real-time simulations, and to match a user's visual responses to those the user would experience in a real-world scene. Large, abrupt changes in scene intensities can cause dramatic compression of visual responses, followed by a gradual recovery of normal vision. Asymmetric mechanisms govern these time-dependent adjustments, and offer adaptation to increased light that is much more rapid than adjustment to darkness. We derive a new tone reproduction operator that simulates these mechanisms. The operator accepts a stream of scene intensity frames and creates a stream of color display images. All operator components are derived from published quantitative measurements from physiology, psychophysics, color science, and photography. ept intentionally simple to allow fast computation, the operator is meant for use with real-time walk-through renderings, high dynamic range video cameras, and other interactive applications. We demonstrate its performance on both synthetically generated and acquired “real-world” scenes with large dynamic variations of illumination and contrast.


CITED BY  28

Collaborative Colleagues:
Sumanta N. Pattanaik: colleagues
Jack Tumblin: colleagues
Hector Yee: colleagues
Donald P. Greenberg: colleagues