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Laboratoire d'InfoRmatique en Image et Systèmes d'informationLIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de Lyon

Université Claude Bernard Lyon 1, bâtiment Nautibus43, boulevard du 11 novembre 1918 — F-69622 Villeurbanne cedex

http://liris.cnrs.fr

UMR 5205

Une mesure de texture géométrique pour cacher les artefacts en compression et

tatouage 3D

Guillaume Lavoué

GDR ISIS – Thème D – Compression d'Objets 3D Statiques et Animés – 2 Avril 2009

2

Many processing operations on 3D objects

SimplificationSimplification

CompressionCompression

WatermarkinWatermarkingg

Distorted objectsDistorted objects

These processes must concerve the These processes must concerve the visual aspectvisual aspect of the of the models.models.

Classic geometric distances Classic geometric distances do not correlatedo not correlate with the with the human visual perception human visual perception

3

Masking and Roughness concepts

Our objective is to exploit some perceptual aspects to hide Our objective is to exploit some perceptual aspects to hide degradations produced by standard operations.degradations produced by standard operations.

This idea is linked with the concept of This idea is linked with the concept of MaskingMasking : : A rough A rough region is able to hide some geometric distorsion with similar region is able to hide some geometric distorsion with similar frequencies.frequencies.

In Computer Graphics In Computer Graphics MaskingMasking was investigated by was investigated by Ferwerda et al. 1997 Ferwerda et al. 1997 Complex computational masking Complex computational masking model.model.

Our objective: Our objective: A simple roughness estimatorA simple roughness estimator, allowing to , allowing to concentrate the distorsion of common operations on concentrate the distorsion of common operations on noisednoised areas associated with high masking levels.areas associated with high masking levels.

4

Outline

Introduction

The proposed roughness measure

Results and application to masking

Integration to compression / watermarking

5

Overview

Two main constraints:Two main constraints:

Our measure has to be Our measure has to be Multi-Scale and independent of Multi-Scale and independent of the mesh connectivity.the mesh connectivity.

EdgeEdge and and smoothsmooth regions have to be clearly regions have to be clearly differentiated from differentiated from roughrough regions. regions.

6

Overview

Over local windows

7

Discrete Curvature calculation Geometric information Geometric information is not relatedis not related to perception to perception

Curvature variations strongly reflect the variations of the Curvature variations strongly reflect the variations of the intensity image after rendering.intensity image after rendering.

[Cohen-Steiner and J. Morvan, 2003]Restricted delaunay triangulations and normal cycle

Curvature tensor at each vertex of the meshCurvature tensor at each vertex of the mesh

EigenvaluesEigenvalues Principal curvature values Principal curvature values KminKmin, , KmaxKmax

2ii

i

KminKmaxvC

)(

8

Curvature averaging

9

Adaptive smoothing Main problem with classical smoothing (Laplacian):Main problem with classical smoothing (Laplacian):

Our adaptive smoothingOur adaptive smoothing Derived from the two-step filter Derived from the two-step filter [Taubin, 1995][Taubin, 1995]

Dependent of the sampling density Independent of the sampling density

10

The roughness measure

1.1. The 3D object is smoothed (The 3D object is smoothed (εε scale window) scale window)

2.2. Curvature is calculated for both meshes (original Curvature is calculated for both meshes (original and smoothed)and smoothed)

3.3. Average curvature is processed for each vertex Average curvature is processed for each vertex ((22εε scale window)scale window)

4.4. Asymmetric curvature difference for each vertex Asymmetric curvature difference for each vertex Roughness map Roughness map

11

Outline

Introduction

The proposed roughness measure

Results and application to masking

Integration to compression / watermarking

12

Results

ε = 1 % ε = 3 %

13

Comparison

14

Robustness to connectivity change

Sam

pling density

15

Application to Masking

Original Two clustersRough / Smooth

Noise on smooth regions

Noise on rough regions

Same RMS distance

Much more visibleMSDM = 0,42

MSDM = 0,36

Rough regions exhibit a higher masking degree.Rough regions exhibit a higher masking degree. Distorsion errors coming from common processing operations Distorsion errors coming from common processing operations

can be concentrated on these areas.can be concentrated on these areas.

16

Subjective experimentThe 3D corpus 4 objects 6 versions : 3 noise strengths

on smooth and rough areas

Evaluation protocol 6 degraded versions are displayed to the observer together with

the original object He must provide a score between 4 (identical to the original) and 0

(worst case)

Results

17

Outline

Introduction

The proposed roughness measure

Results and application to masking

Integration to compression / watermarking

18

Integration to single rate compression

Roughness analysis

19

The algorithm

Connectivity coding Face Fixer [Isenburg and Snoeyink 2001]

Geometry coding Simple differential coding Variable quantization: lower for rough region, higher for smooth ones

Arithmetic coding

Roughness classification Markov based clustering [Lavoué and Wolf 2008]

20

Results

21

Integration to spectral watermarking

The Ohbuchi et al. [2002] non blind scheme:

Mesh segmentation into patches

Spectral decomposition of each patch

Modulation of spectral coefficients (fixed strength α)

Non blind extraction

Watermark WatermarkWatermark

Roughness analysis

22

Illustration

Roughness map Segmented regions

Adaptation of the VSA [Cohen-Steiner et al. 2004]

23

Visual results

Original Ohbuchi et al; 2002 Ours

24

Robustness

2 attacks Noise addition Non uniform scaling 50 insertion / extraction

25

Conclusion

Un algorithme de caractérisation de la rugosité de la surfaceMise en évidence du phénomène de Masking par une expérience subjectiveRésultats encourageants après intégration pour la compression et le tatouagePour + d’info: Lavoué, G. 2009. A local roughness measure for 3D meshes and its application to visual masking. ACM Trans. Appl. Percept. 5, 4 (Jan. 2009), 1-23.

Et maintenant : Caractérisation plus théorique du phénomène par des expériences subjectives plus poussées