Article

Visual equivalence: towards a new standard for image fidelity

Authors:

Ganesh Ramanarayanan,

James Ferwerda,

Kavita BalaAuthors Info & Claims

SIGGRAPH '07: ACM SIGGRAPH 2007 papers

Pages 76 - es

https://doi.org/10.1145/1275808.1276472

Published: 29 July 2007 Publication History

Abstract

Efficient, realistic rendering of complex scenes is one of the grand challenges in computer graphics. Perceptually based rendering addresses this challenge by taking advantage of the limits of human vision. However, existing methods, based on predicting visible image differences, are too conservative because some kinds of image differences do not matter to human observers. In this paper, we introduce the concept of visual equivalence, a new standard for image fidelity in graphics. Images are visually equivalent if they convey the same impressions of scene appearance, even if they are visibly different. To understand this phenomenon, we conduct a series of experiments that explore how object geometry, material, and illumination interact to provide information about appearance, and we characterize how two kinds of transformations on illumination maps (blurring and warping) affect these appearance attributes. We then derive visual equivalence predictors (VEPs): metrics for predicting when images rendered with transformed illumination maps will be visually equivalent to images rendered with reference maps. We also run a confirmatory study to validate the effectiveness of these VEPs for general scenes. Finally, we show how VEPs can be used to improve the efficiency of two rendering algorithms: Light-cuts and precomputed radiance transfer. This work represents some promising first steps towards developing perceptual metrics based on higher order aspects of visual coding.

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Cited By

Najimudeen APeters AMonga MLonati VBarendsen ESheard JPaterson J(2024)Exploring Perception in Computer Graphics Education: A Swedish PerspectiveProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653592(743-749)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653592
Lavoué GBonneel NFarrugia JSoler C(2021)Perceptual quality of BRDF approximations: dataset and metricsComputer Graphics Forum10.1111/cgf.14263640:2(327-338)Online publication date: 4-Jun-2021
https://doi.org/10.1111/cgf.142636
Lu YLuo LHuang DWang YChen L(2020)Knowledge Transfer in Vision RecognitionACM Computing Surveys10.1145/337934453:2(1-35)Online publication date: 17-Apr-2020
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Index Terms

Visual equivalence: towards a new standard for image fidelity
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging
  2. Computer graphics
    1. Animation

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cover image ACM Conferences

SIGGRAPH '07: ACM SIGGRAPH 2007 papers

August 2007

1019 pages

ISBN:9781450378369

DOI:10.1145/1275808

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Published: 29 July 2007

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SIGGRAPH '07 Paper Acceptance Rate 108 of 455 submissions, 24%;

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Najimudeen APeters AMonga MLonati VBarendsen ESheard JPaterson J(2024)Exploring Perception in Computer Graphics Education: A Swedish PerspectiveProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653592(743-749)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653592
Lavoué GBonneel NFarrugia JSoler C(2021)Perceptual quality of BRDF approximations: dataset and metricsComputer Graphics Forum10.1111/cgf.14263640:2(327-338)Online publication date: 4-Jun-2021
https://doi.org/10.1111/cgf.142636
Lu YLuo LHuang DWang YChen L(2020)Knowledge Transfer in Vision RecognitionACM Computing Surveys10.1145/337934453:2(1-35)Online publication date: 17-Apr-2020
https://dl.acm.org/doi/10.1145/3379344
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