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Design of a Tone Mapping Operator for High Dynamic Range Images based
Design of a Tone Mapping Operator for High Dynamic Range Images based
Overview
Overview
Motivation
Motivation
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
Goals
Goals
Various Classifications
Various Classifications
Previous Work: Global Methods
Previous Work: Global Methods
Examples
Examples
Previous Work: Local Methods
Previous Work: Local Methods
Examples
Examples
Examples
Examples
Ashikhmin
Ashikhmin
Psychophysical Experiment
Psychophysical Experiment
Design of a Tone Mapping Operator for High Dynamic Range Images based
Design of a Tone Mapping Operator for High Dynamic Range Images based
Statistical Data Processing
Statistical Data Processing
Subject Preferences
Subject Preferences
Retinex
Retinex
Retinex Extensions: for HDR
Retinex Extensions: for HDR
Halo Reduction: Retinex Rotation
Halo Reduction: Retinex Rotation
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Retinex Maximum Reset
Retinex Maximum Reset
Linear mapping
Linear mapping
Retinex + Tone Mapping Op
Retinex + Tone Mapping Op
Logmap Equation
Logmap Equation
Adaptive Logarithmic Mapping
Adaptive Logarithmic Mapping
Conclusions
Conclusions
Color Balance Correction
Color Balance Correction
Stanford Memorial Church Photograph
Stanford Memorial Church Photograph
Stanford Memorial Church Photograph
Stanford Memorial Church Photograph
Acknowledgments
Acknowledgments

Презентация: «Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping». Автор: Max-Planck-Institut fuer Informatik. Файл: «Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping.ppt». Размер zip-архива: 10914 КБ.

Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping

содержание презентации «Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping.ppt»
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1 Design of a Tone Mapping Operator for High Dynamic Range Images based

Design of a Tone Mapping Operator for High Dynamic Range Images based

upon Psychophysical Evaluation and Preference Mapping

F. Drago1, W. Martens2, K. Myszkowski3, and N. Chiba1 1Iwate University and 2Aizu University, Japan 3Max-Planck-Institut f?r Informatik, Germany

2 Overview

Overview

Motivation Previous work Psychophysical experiment Enhancements of Retinex for HDR images Conclusions

3 Motivation

Motivation

Many applications Lighting simulation and realistic rendering High Dynamic Range photography Multimedia: distributing HDR video streams

4 HDR Photographs + Rendering: Real World Lighting

HDR Photographs + Rendering: Real World Lighting

1) Photographs of mirror sphere at varying exposure times

3) Use as light source in Monte Carlo radiosity algorithm

2) High-dynamic range environment map

Philippe Bekaert

5 Goals

Goals

Technical requirement Match the dynamic range of image to the range available on a given display device Various objectives Get good perceptual match between the real-world and corresponding images Reproducing details Maximize reproducible contrast Just to get “nice-looking” images

6 Various Classifications

Various Classifications

Theoretical foundations Perception-based Pure image processing techniques Mapping function Global – the same for all pixels Local – depends on local image contents Temporal processing Static Dynamic

7 Previous Work: Global Methods

Previous Work: Global Methods

Perception-based Tumblin and Rushmeier (1993,1999) Brightness matching Ward (1994), Ferwerda et al. (1996) Contrast matching (a linear function is used) Ward et al. (1997) Adjusting image histogram to avoid exceeding display contrast in respect to the real-world scene Efficiency-driven Schlick (1994) Rational functions

8 Examples

Examples

Ferwerda et al. Tumblin (1999) Ward et al. Schlick

9 Previous Work: Local Methods

Previous Work: Local Methods

Early methods – prone to halo artifacts Chiu et al. (1993), Schlick (1994), Land (1971), Jobson et al. (1997): Retinex Pattanaik et al. (1998): The most comprehensive model of HVS used in CG LCIS: Tumblin and Turk (1999) Based on an anisotropic diffusion procedure Emphasize on details but compress excessively contrast New wave: Fattal et al., Reinhard et al., Durand and Dorsey, Ashikhmin (2002)

10 Examples

Examples

Tumblin and Turk

Ashikhmin

Retinex

11 Examples

Examples

Durand and Dorsey

Fattal et al.

Reinhard et al.

12 Ashikhmin

Ashikhmin

Durand and Dorsey

Fattal et al.

Reinhard et al.

13 Psychophysical Experiment

Psychophysical Experiment

Perceptual evaluation of subject preference by pairwise comparison of tone mapped images Seven tone mapping algorithms examined: Tumblin and Rushmeier (1993), Ferwerda et al. (1996), Ward et al. (1997), Schlick (1994), Retinex - based on Funt and Ciurea (2001) implementation but with our extensions toward suppressing halo Reinhard et al. (2002) – photographic method Tumblin and Turk (1999) - LCIS Four scenes considered

14 Design of a Tone Mapping Operator for High Dynamic Range Images based
15 Statistical Data Processing

Statistical Data Processing

11 subjects participated Dissimilarity ratings for pairwise comparisons of images submitted to Individual Differences Scaling (INDSCAL) analysis Stimulus Space configures the stimuli such that Euclidian distances between the stimuli match the obtained dissimilarity judgments Axes labeled based upon correlation of the dimensional coordinates with independently generated attribute ratings (naturalness, detail and contrast reproduction) “Ideal” preference point obtained through PREFMAP analysis

16 Subject Preferences

Subject Preferences

T: Tumblin & R. V: Ferwerda et al. H: Ward et al. Q: Schlick X: Retinex P: Reinhard et al.

17 Retinex

Retinex

We use the “Frankle-McCann Retinex” algorithm ratio-product-reset-average NP(x,y) new pixel value is obtained from the original image R() and previous iteration image OP() as follows: Reset test In each iteration (the number of iterations predefined by the user) the distance D between pixels (x,y) and (xs,ys) is halved the direction for pixel comparison is rotated 90o clockwise Main problem: Suppressing halo effects

18 Retinex Extensions: for HDR

Retinex Extensions: for HDR

Main problem: Suppressing halo effects Adding counterclockwise rotation of the path suggested by Coopers Spatially varying levels of pixel interaction based contrast information Suggested by Sobol, but we use a smooth function for clipping Adjusting a reset ratio to the maximum luminance of the display device instead of the maximum luminance of the scene

19 Halo Reduction: Retinex Rotation

Halo Reduction: Retinex Rotation

Clockwise

CounterClockwise

Both Ways

All images for 40 iterations

20 Halo Reduction: Retinex Contrast Crop with Bias

Halo Reduction: Retinex Contrast Crop with Bias

21 Halo Reduction: Retinex Contrast Crop with Bias

Halo Reduction: Retinex Contrast Crop with Bias

Standard Retinex 33 iterations cw and ccw

The same settings but crop with bias added

22 Halo Reduction: Retinex Contrast Crop with Bias

Halo Reduction: Retinex Contrast Crop with Bias

33 Retinex iterations

33 Retinex iterations

23 Halo Reduction: Retinex Contrast Crop with Bias

Halo Reduction: Retinex Contrast Crop with Bias

4 Retinex iterations

30 Retinex iterations

24 Retinex Maximum Reset

Retinex Maximum Reset

Maximum = 226.5 cd/m^2

Maximum = 100 cd/m^2

25 Linear mapping

Linear mapping

Retinex 4 iterations

Extended Retinex 4 iterations

Extended Retinex 4 iterations

26 Retinex + Tone Mapping Op

Retinex + Tone Mapping Op

Ferwerda et al. (1996)

Logmap - new

27 Logmap Equation

Logmap Equation

28 Adaptive Logarithmic Mapping

Adaptive Logarithmic Mapping

Performance: Software 30 fps on PentiumIV, 2.2GHz Hardware ?

29 Conclusions

Conclusions

We performed psychophysical of seven existing tone mapping operators. More details in our TechRep: http://data.mpi-sb.mpg.de/internet/reports.nsf/AG4NumberView?OpenView Good performance of Retinex in the experiment encouraged us extend it toward reducing hallo artifacts Addind a regular tone mapping processing atop of Retinex results make the resulting images more independent on the number of Retinex iterations and improve the image naturalness Future work: repeating psychophysical with all recent local tone mapping operators and our extended Retinex

30 Color Balance Correction

Color Balance Correction

Retinex Applied to All Channels in LMS Color Space

31 Stanford Memorial Church Photograph

Stanford Memorial Church Photograph

32 Stanford Memorial Church Photograph

Stanford Memorial Church Photograph

33 Acknowledgments

Acknowledgments

We would like to thank Michael Ashikhmin, Paul Debevec, Fredo Durand, Dani Lischinski, Eric Reinhard, and Greg Ward for providing us with some images used in this presentation. We would like also to thank Greg Ward for his precious comments concerning our work.

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