In this paper, a scalable intraband wavelet coding technique for semi-regular topology surface meshes is proposed. The choice of an intraband codec design is justified through an information theoretic analysis of the statistical dependencies between the wavelet coefficients resulting from a wavelet-based decomposition of the input 3D mesh. The bitstream produced by the proposed codec is both resolution and quality scalable. This lies in contrast with well-known zero-tree based interband coding techniques that only support quality scalability. The experimental results show that, on average, the proposed codec outperforms the state-of-the-art zero-tree based interband codec in rate-distortion sense when using the L2 norm as target distortion metric.
"This was later extended in  proposing scalable intraband and composite wavelet-based mesh coding technologies. Most existing coding techniques, including those of ,  and , employ the L-2 distortion metric      . The L-2 metric provides a good approximation of the global error, without giving any indication about the magnitude of the local coding error on each vertex. "
[Show abstract][Hide abstract] ABSTRACT: Off-line scanning, coding, transmission and remote animation of the human affect represents a possible processing pipeline for providing 3D immersion in virtual worlds. In this paper we target applications that make use of compact and scalable 3D representations of human affect and require close control over the local error introduced by lossy coding of the mesh geometry. To satisfy this requirement, we propose a novel L-infinite wavelet-based semi-regular mesh coding system. The system lies in contrast with classical mesh coding approaches which make use of the L-2 distortion metric. Specifically, in contrast to an L-2 driven implementation, the proposed system provides a bound on the local error on each vertex resulting from scalar embedded quantization of the wavelet coefficients. The experiments show that the proposed system provides scalability in L-infinite sense and that it outperforms the state-of-the art in L-infinite mesh coding.
[Show abstract][Hide abstract] ABSTRACT: In this paper, a Laplacian Mixture (LM) model is proposed to accurately approximate the observed histogram of the wavelet
coefficients produced by lifting-based subdivision wavelet transforms. On average, the proposed mixture model gives better
histogram fitting for both normal and non-normal meshes compared to the traditionally used Generalized Gaussian (GG) distributions.
Exact closed-form expressions for the rate and the distortion of the LM probability density function quantized using generic
embedded deadzone scalar quantizer (EDSQ) are derived, without making high-rate assumptions. Experimental evaluations carried
out on a set of 3D meshes reveals that, on average, the D-R function for the LM model closely follows and gives a better indication
of the experimental D-R compared to the D-R curve of the competing GG model. Optimal embedded quantization for the proposed
LM model is experimentally determined. In this sense, it is concluded that the classical Successive Approximation Quantization
(SAQ) is an acceptable, but in general, not an optimal embedded quantization solution in wavelet-based scalable coding of
Advanced Concepts for Intelligent Vision Systems - 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I; 01/2010
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