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Proceedings Paper

Scalable image coding with fine granularity based on hierarchical mesh
Author(s): Patrick Lechat; Nathalie Laurent; Henri Sanson
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Paper Abstract

This paper presents a method for still image encoding based on a hierarchical mesh representation. Contrary to most classical coding schemes which transform the signal into the frequential domain and quantize it, our method performs a purely spatial, content adaptive, representation. The main goals are: both spatial and SNR scalability, progressive bitstream transmission and efficient support for motion estimation and compensation. The technique presented consists in approximating the image by triangular mesh covering the whole image domain, which allows to use the finite elements method. Mesh nodes carry both position information and photometric data (YUV) and the Lagrangian affine interpolation model defined on triangular elements enables image approximation everywhere. To perform the scalability and the content adaptive scheme, the base level mesh is iteratively subdivided, by splitting each triangle into 4 new ones. Furthermore, to decrease the coding rate, mesh nodes position and values are quantized and differential encoded across mesh levels. A quad tree built during mesh subdivision selects and sorts data to be sent to the bitstream, given a quality criteria per tree node. By this way, the most important information is sent first, delivering a rough image representation, then further differential values are transmitted to enhance the representation quality.

Paper Details

Date Published: 28 December 1998
PDF: 13 pages
Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); doi: 10.1117/12.334620
Show Author Affiliations
Patrick Lechat, France Telecom CNET (France)
Nathalie Laurent, France Telecom CNET (France)
Henri Sanson, France Telecom CNET (France)


Published in SPIE Proceedings Vol. 3653:
Visual Communications and Image Processing '99
Kiyoharu Aizawa; Robert L. Stevenson; Ya-Qin Zhang, Editor(s)

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