on the efficiency of image metrics for evaluating the visual quality of 3d models
DESCRIPTION
On the Efficiency of Image Metrics for Evaluating the Visual Quality of 3D Models. Guillaume Lavoué. Mohamed Chaker Larabi. Libor Vasa. Université de Lyon LIRIS. Université de poitier XLIM-SIC. University of West Bohemia. An illustration. Smoothing Taubin , 2000. Watermarking - PowerPoint PPT PresentationTRANSCRIPT
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UMR 5205
On the Efficiency of Image Metrics forEvaluating the Visual Quality of 3D
Models
Université de LyonLIRIS
Guillaume LavouéMohamed Chaker Larabi Libor VasaUniversité de poitier
XLIM-SICUniversity of West Bohemia
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Original
An illustration
Watermarking Cho et al. 2006 Noise addition
Simplification Lindstrom, Turk 2000
Watermarking Wang et al. 2011
Smoothing Taubin, 2000
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Same Max Root Mean Square Error (1.05 × 10-3)
0.14 0.40
0.62 0.840.51
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Quality metrics for static meshes
Local curvature statistics
MatchingLocal Distortion
Map
Local differences of statistics
Spatial pooling
Global Distortion Score
Distorted model
Original model
MSDM [Lavoué et al. 2006]MSDM2 [Lavoué 2011][Torkhani et al. 2012]
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Our previous works
Distortion score
Why not using Image Quality Metrics? Such image-based approach has been already used for driving simplification[Lindstrom, Turk, 2000][Qu, Meyer, 2008]
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Our study
Determine the best set of parameters to use for such image-based quality assessment approach.
Compare this approach to the most performing model-based metrics.
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Many parameters
Which 2D metric to use? How many views, which views? How to combine the 2D scores? Which rendering, lighting?
In our study, we consider:o 6 image metricso 2 rendering algorithmso 9 lighting conditionso 5 ways of combining image metric resultso 4 databases to evaluate the results
Around 100,000 images
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Image Quality Metrics
Simple PSNR and Root Mean Square Error MSSIM (multi-scale SSIM) [Wang et al. 2003] VIF (visual information fidelity) [Sheikh and Bovik, 2006] IWSSIM (information content weighted SSIM) [Wang and LI, 2011] FSIM (feature similarity index) [Zhang et al. 2011]
State of the art algorithms
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Generation of 2D views and lightning conditions
42 cameras placed uniformly around the object
Rendering using a single white directional light source
The light is either fixed with respect to the camera, or with respect to the object
3 positions: front, top, top-right
So we have 3*2 = 6 lighting conditions We also consider averages of object-light, camera-
light and global 9 conditions
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Image Rendering Protocols
We consider 2 ways of computing the normals, with or without averaging on the neighborhood.
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Pooling algorithms
How to combine the per-image quality score into a single one?
Minkowski norm is popular:
We also consider image importance weights
[Secord et al. 2011]Perceptual model of viewpoint preference Surface visibility
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The MOS databases
The LIRIS/EPFL General-Purpose Database 88 models (from 40K to 50K vertices) from 4 reference objects.
Non uniform noise addition and smoothing.
The LIRIS Masking Database 26 models (from 9K to 40K vertices) from 4 reference objects.
Noise addition on smooth or rough regions.
The IEETA Simplification Database 30 models (from 2K to 25K vertices) from 5 reference objects.
Three simplification algorithms.
The UWB Compression database 68 models from 5 reference objects
Different kinds of artefacts from compression
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Results and analysis
Basically we have a full factorial experiments heavily used in statistics to study the effect of different factors on a response variable
We consider 4 factors: o The metric (6 possible values)o The lighting (9 possible values)o The pooling (5 possible values)o The rendering (2 possible values).
540 possible combinations
We consider two response variables:o Sperman correlation over all the objectso Sperman correlation averaged per objects
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Results and analysis
For a given factor associated with n possible values, we have n
sets of paired spearman coefficients.
To estimate the effect of a given factor on the objective metric performance, we conduct pairwise comparisons of each of its value between the others (i.e. n(n-1)/2 comparisons).
We have paired values, so we can do better than a simple comparison of the means. Statistical significance test (not Student but Wilcoxon signed rank test). We study the median of paired differences, as well as the 25th and 75th percentiles.
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Influence of the metrics
IWSSIM provides the best results FSIM and MSSIM are 2nd best, significantlky better than
MSE and PSNR. VIF provides instable results (see the percentiles).
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Influence of the lighting
Indirect illuminations provide better results Light has to be linked to the camera Object-front is not so bad, but not its performances
are not stable.
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Influence of the pooling
Low values of P are better. Weights do not bring significant
improvments.
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Comparisons with 3D metrics
For easy scenarios: 2D metrics are excellent
However when the task becomes more difficult, 3D metrics are better
But, still, simple image-based metrics are better than simple geometric ones.