- Laboratoire d'InfoRmatique en Image et Systèmes d'information LIRIS 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 Nautibus 43, boulevard du 11 novembre 1918 F Villeurbanne cedex 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 Simplification Compression Watermarking Distorted objects These processes must concerve the visual aspect of the models. These processes must concerve the visual aspect of the models. Classic geometric distances do not correlate with the human visual perception Classic geometric distances do not correlate with the human visual perception

3 Masking and Roughness concepts Our objective is to exploit some perceptual aspects to hide degradations produced by standard operations. Our objective is to exploit some perceptual aspects to hide degradations produced by standard operations. This idea is linked with the concept of Masking : A rough region is able to hide some geometric distorsion with similar frequencies. This idea is linked with the concept of Masking : A rough region is able to hide some geometric distorsion with similar frequencies. In Computer Graphics Masking was investigated by Ferwerda et al Complex computational masking model. In Computer Graphics Masking was investigated by Ferwerda et al Complex computational masking model. Our objective: A simple roughness estimator, allowing to concentrate the distorsion of common operations on noised areas associated with high masking levels. Our objective: A simple roughness estimator, allowing to concentrate the distorsion of common operations on noised 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 Multi-Scale and independent of the mesh connectivity. Our measure has to be Multi-Scale and independent of the mesh connectivity. Edge and smooth regions have to be clearly differentiated from rough regions. Edge and smooth regions have to be clearly differentiated from rough regions.

6 Overview Over local windows

7 Discrete Curvature calculation Geometric information is not related to perception Geometric information is not related to perception Curvature variations strongly reflect the variations of the intensity image after rendering. Curvature variations strongly reflect the variations of the intensity image after rendering. [Cohen-Steiner and J. Morvan, 2003] Restricted delaunay triangulations and normal cycle Curvature tensor at each vertex of the mesh Eigenvalues Principal curvature values Kmin, Kmax

8 Curvature averaging

9 Adaptive smoothing Main problem with classical smoothing (Laplacian): Main problem with classical smoothing (Laplacian): Our adaptive smoothing Our adaptive smoothing Derived from the two-step filter [Taubin, 1995] Derived from the two-step filter [Taubin, 1995] Dependent of the sampling densityIndependent of the sampling density

10 The roughness measure 1. The 3D object is smoothed (ε scale window) 2. Curvature is calculated for both meshes (original and smoothed) 3. Average curvature is processed for each vertex (2ε scale window) 4. Asymmetric curvature difference for each vertex 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 Sampling density

15 Application to Masking OriginalTwo clusters Rough / Smooth Noise on smooth regions Noise on rough regions Same RMS distance Much more visible MSDM = 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 can be concentrated on these areas. Distorsion errors coming from common processing operations can be concentrated on these areas.

16 Subjective experiment The 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 Roughness analysis

22 Illustration Roughness map Segmented regions Adaptation of the VSA [Cohen-Steiner et al. 2004]

23 Visual results OriginalOhbuchi 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 surface Mise en évidence du phénomène de Masking par une expérience subjective Résultats encourageants après intégration pour la compression et le tatouage Pour + dinfo: Lavoué, G A local roughness measure for 3D meshes and its application to visual masking. ACM Trans. Appl. Percept. 5, 4 (Jan. 2009), Et maintenant : Caractérisation plus théorique du phénomène par des expériences subjectives plus poussées