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The visible differences predictor: an algorithm for the assessment of image fidelity

Author:

Scott DalyAuthors Info & Claims

Digital images and human vision

October 1993

Pages 179 - 206

Published: 29 October 1993 Publication History

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Chizhov VGeorgiev IMyszkowski KSingh G(2022)Perceptual Error Optimization for Monte Carlo RenderingACM Transactions on Graphics10.1145/350400241:3(1-17)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3504002
Artusi ABanterle FCarra FMoreno A(2020)Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual MetricsIEEE Transactions on Image Processing10.1109/TIP.2019.294407929(1843-1855)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1109/TIP.2019.2944079
Whittle JJones MLaramee RTam GVidal F(2018)A deep learning approach to no-reference image quality assessment for Monte Carlo rendered imagesProceedings of the Conference on Computer Graphics & Visual Computing10.2312/cgvc.20181204(23-31)Online publication date: 13-Sep-2018
https://dl.acm.org/doi/10.2312/cgvc.20181204
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Index Terms

The visible differences predictor: an algorithm for the assessment of image fidelity
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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Published In

Digital images and human vision

October 1993

224 pages

ISBN:0262231719

Editor:
Andrew B. Watson
NASA Ames Research Center, Moffett Field, CA

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 29 October 1993

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Chizhov VGeorgiev IMyszkowski KSingh G(2022)Perceptual Error Optimization for Monte Carlo RenderingACM Transactions on Graphics10.1145/350400241:3(1-17)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3504002
Artusi ABanterle FCarra FMoreno A(2020)Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual MetricsIEEE Transactions on Image Processing10.1109/TIP.2019.294407929(1843-1855)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1109/TIP.2019.2944079
Whittle JJones MLaramee RTam GVidal F(2018)A deep learning approach to no-reference image quality assessment for Monte Carlo rendered imagesProceedings of the Conference on Computer Graphics & Visual Computing10.2312/cgvc.20181204(23-31)Online publication date: 13-Sep-2018
https://dl.acm.org/doi/10.2312/cgvc.20181204
Wolski KGiunchi DYe NDidyk PMyszkowski KMantiuk RSeidel HSteed AMantiuk R(2018)Dataset and Metrics for Predicting Local Visible DifferencesACM Transactions on Graphics10.1145/319649337:5(1-14)Online publication date: 26-Nov-2018
https://dl.acm.org/doi/10.1145/3196493
Kerouh FZiou DSerir A(2018)Histogram modelling-based no reference blur quality measureImage Communication10.1016/j.image.2017.08.01460:C(22-28)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.image.2017.08.014
Bonneel NKovacs BParis SBala K(2017)Intrinsic Decompositions for Image EditingComputer Graphics Forum10.5555/3128975.312902736:2(593-609)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.5555/3128975.3129027
Weier MStengel MRoth TDidyk PEisemann EEisemann MGrogorick SHinkenjann AKruijff EMagnor MMyszkowski KSlusallek P(2017)Perception-driven Accelerated RenderingComputer Graphics Forum10.1111/cgf.1315036:2(611-643)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1111/cgf.13150
Harvey CDebattista KBashford-Rogers TChalmers A(2017)Multi-Modal Perception for Selective RenderingComputer Graphics Forum10.1111/cgf.1279336:1(172-183)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1111/cgf.12793
Hill PAl-Mualla MBull D(2017)Perceptual Image Fusion Using WaveletsIEEE Transactions on Image Processing10.1109/TIP.2016.263386326:3(1076-1088)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TIP.2016.2633863
Safavi STorkamani-Azar F(2017)Cube-based perceptual weighted Kronecker Compressive SensingJournal of Visual Communication and Image Representation10.1016/j.jvcir.2017.08.01049:C(338-350)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.jvcir.2017.08.010
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Learning-aided predictor integration for system performance prediction

A new case for the TAGE branch predictor

The Cascaded Predictor: Economic and Adaptive BranchTarget Prediction

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