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Motivation
Motivation
Motivation
Motivation
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
HDR Photographs + Rendering: Real World Lighting
Goals
Goals
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Examples
Ashikhmin
Ashikhmin
Ashikhmin
Ashikhmin
Ashikhmin
Ashikhmin
Ashikhmin
Ashikhmin
Design of a Tone Mapping Operator for High Dynamic Range Images based
Design of a Tone Mapping Operator for High Dynamic Range Images based
Subject Preferences
Subject Preferences
Halo Reduction: Retinex Rotation
Halo Reduction: Retinex Rotation
Halo Reduction: Retinex Rotation
Halo Reduction: Retinex Rotation
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
Halo Reduction: Retinex Contrast Crop with Bias
Halo Reduction: Retinex Contrast Crop with Bias
Retinex Maximum Reset
Retinex Maximum Reset
Retinex Maximum Reset
Retinex Maximum Reset
Linear mapping
Linear mapping
Retinex + Tone Mapping Op
Retinex + Tone Mapping Op
Logmap Equation
Logmap Equation
Logmap Equation
Logmap Equation
Adaptive Logarithmic Mapping
Adaptive Logarithmic Mapping
Color Balance Correction
Color Balance Correction
Stanford Memorial Church Photograph
Stanford Memorial Church Photograph
Stanford Memorial Church Photograph
Stanford Memorial Church Photograph
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Автор: Max-Planck-Institut fuer Informatik. Чтобы познакомиться с картинкой полного размера, нажмите на её эскиз. Чтобы можно было использовать все картинки для урока физики, скачайте бесплатно презентацию «Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping.ppt» со всеми картинками в zip-архиве размером 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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1Design of a Tone Mapping Operator for 15dimensional coordinates with independently
High Dynamic Range Images based upon generated attribute ratings (naturalness,
Psychophysical Evaluation and Preference detail and contrast reproduction) “Ideal”
Mapping. F. Drago1, W. Martens2, K. preference point obtained through PREFMAP
Myszkowski3, and N. Chiba1 1Iwate analysis.
University and 2Aizu University, Japan 16Subject Preferences. T: Tumblin &
3Max-Planck-Institut f?r Informatik, R. V: Ferwerda et al. H: Ward et al. Q:
Germany. Schlick X: Retinex P: Reinhard et al.
2Overview. Motivation Previous work 17Retinex. We use the “Frankle-McCann
Psychophysical experiment Enhancements of Retinex” algorithm
Retinex for HDR images Conclusions. ratio-product-reset-average NP(x,y) new
3Motivation. Many applications Lighting pixel value is obtained from the original
simulation and realistic rendering High image R() and previous iteration image
Dynamic Range photography Multimedia: OP() as follows: Reset test In each
distributing HDR video streams. iteration (the number of iterations
4HDR Photographs + Rendering: Real predefined by the user) the distance D
World Lighting. 1) Photographs of mirror between pixels (x,y) and (xs,ys) is halved
sphere at varying exposure times. 3) Use the direction for pixel comparison is
as light source in Monte Carlo radiosity rotated 90o clockwise Main problem:
algorithm. 2) High-dynamic range Suppressing halo effects.
environment map. Philippe Bekaert. 18Retinex Extensions: for HDR. Main
5Goals. Technical requirement Match the problem: Suppressing halo effects Adding
dynamic range of image to the range counterclockwise rotation of the path
available on a given display device suggested by Coopers Spatially varying
Various objectives Get good perceptual levels of pixel interaction based contrast
match between the real-world and information Suggested by Sobol, but we use
corresponding images Reproducing details a smooth function for clipping Adjusting a
Maximize reproducible contrast Just to get reset ratio to the maximum luminance of
“nice-looking” images. the display device instead of the maximum
6Various Classifications. Theoretical luminance of the scene.
foundations Perception-based Pure image 19Halo Reduction: Retinex Rotation.
processing techniques Mapping function Clockwise. CounterClockwise. Both Ways.
Global – the same for all pixels Local – All images for 40 iterations.
depends on local image contents Temporal 20Halo Reduction: Retinex Contrast Crop
processing Static Dynamic. with Bias.
7Previous Work: Global Methods. 21Halo Reduction: Retinex Contrast Crop
Perception-based Tumblin and Rushmeier with Bias. Standard Retinex 33 iterations
(1993,1999) Brightness matching Ward cw and ccw. The same settings but crop
(1994), Ferwerda et al. (1996) Contrast with bias added.
matching (a linear function is used) Ward 22Halo Reduction: Retinex Contrast Crop
et al. (1997) Adjusting image histogram to with Bias. 33 Retinex iterations. 33
avoid exceeding display contrast in Retinex iterations.
respect to the real-world scene 23Halo Reduction: Retinex Contrast Crop
Efficiency-driven Schlick (1994) Rational with Bias. 4 Retinex iterations. 30
functions. Retinex iterations.
8Examples. Ferwerda et al. Tumblin 24Retinex Maximum Reset. Maximum = 226.5
(1999) Ward et al. Schlick. cd/m^2. Maximum = 100 cd/m^2.
9Previous Work: Local Methods. Early 25Linear mapping. Retinex 4 iterations.
methods – prone to halo artifacts Chiu et Extended Retinex 4 iterations. Extended
al. (1993), Schlick (1994), Land (1971), Retinex 4 iterations.
Jobson et al. (1997): Retinex Pattanaik et 26Retinex + Tone Mapping Op. Ferwerda et
al. (1998): The most comprehensive model al. (1996). Logmap - new.
of HVS used in CG LCIS: Tumblin and Turk 27Logmap Equation.
(1999) Based on an anisotropic diffusion 28Adaptive Logarithmic Mapping.
procedure Emphasize on details but Performance: Software 30 fps on PentiumIV,
compress excessively contrast New wave: 2.2GHz Hardware ?
Fattal et al., Reinhard et al., Durand and 29Conclusions. We performed
Dorsey, Ashikhmin (2002). psychophysical of seven existing tone
10Examples. Tumblin and Turk. Ashikhmin. mapping operators. More details in our
Retinex. TechRep:
11Examples. Durand and Dorsey. Fattal et http://data.mpi-sb.mpg.de/internet/reports
al. Reinhard et al. nsf/AG4NumberView?OpenView Good
12Ashikhmin. Durand and Dorsey. Fattal performance of Retinex in the experiment
et al. Reinhard et al. encouraged us extend it toward reducing
13Psychophysical Experiment. Perceptual hallo artifacts Addind a regular tone
evaluation of subject preference by mapping processing atop of Retinex results
pairwise comparison of tone mapped images make the resulting images more independent
Seven tone mapping algorithms examined: on the number of Retinex iterations and
Tumblin and Rushmeier (1993), Ferwerda et improve the image naturalness Future work:
al. (1996), Ward et al. (1997), Schlick repeating psychophysical with all recent
(1994), Retinex - based on Funt and Ciurea local tone mapping operators and our
(2001) implementation but with our extended Retinex.
extensions toward suppressing halo 30Color Balance Correction. Retinex
Reinhard et al. (2002) – photographic Applied to All Channels in LMS Color
method Tumblin and Turk (1999) - LCIS Four Space.
scenes considered. 31Stanford Memorial Church Photograph.
14 32Stanford Memorial Church Photograph.
15Statistical Data Processing. 11 33Acknowledgments. We would like to
subjects participated Dissimilarity thank Michael Ashikhmin, Paul Debevec,
ratings for pairwise comparisons of images Fredo Durand, Dani Lischinski, Eric
submitted to Individual Differences Reinhard, and Greg Ward for providing us
Scaling (INDSCAL) analysis Stimulus Space with some images used in this
configures the stimuli such that Euclidian presentation. We would like also to thank
distances between the stimuli match the Greg Ward for his precious comments
obtained dissimilarity judgments Axes concerning our work.
labeled based upon correlation of the
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Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping

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900igr.net > Презентации по физике > Двигатель внутреннего сгорания > Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping