Paper
5 May 2010 Spatially adaptive bases in wavelet-based coding of semi-regular meshes
Leon Denis, Ruxandra Florea, Adrian Munteanu, Peter Schelkens
Author Affiliations +
Abstract
In this paper we present a wavelet-based coding approach for semi-regular meshes, which spatially adapts the employed wavelet basis in the wavelet transformation of the mesh. The spatially-adaptive nature of the transform requires additional information to be stored in the bit-stream in order to allow the reconstruction of the transformed mesh at the decoder side. In order to limit this overhead, the mesh is first segmented into regions of approximately equal size. For each spatial region, a predictor is selected in a rate-distortion optimal manner by using a Lagrangian rate-distortion optimization technique. When compared against the classical wavelet transform employing the butterfly subdivision filter, experiments reveal that the proposed spatially-adaptive wavelet transform significantly decreases the energy of the wavelet coefficients for all subbands. Preliminary results show also that employing the proposed transform for the lowest-resolution subband systematically yields improved compression performance at low-to-medium bit-rates. For the Venus and Rabbit test models the compression improvements add up to 1.47 dB and 0.95 dB, respectively.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Leon Denis, Ruxandra Florea, Adrian Munteanu, and Peter Schelkens "Spatially adaptive bases in wavelet-based coding of semi-regular meshes", Proc. SPIE 7723, Optics, Photonics, and Digital Technologies for Multimedia Applications, 772310 (5 May 2010); https://doi.org/10.1117/12.853584
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Cited by 1 scholarly publication.
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KEYWORDS
Wavelets

Wavelet transforms

Quantization

Venus

Computer programming

Image segmentation

Visualization

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