Scalable image coding with fine granularity based on hierarchical mesh

Patrick Lechat; Nathalie Laurent; Henri Sanson

doi:10.1117/12.334620

28 December 1998 Scalable image coding with fine granularity based on hierarchical mesh

Patrick Lechat, Nathalie Laurent, Henri Sanson

Proceedings Volume 3653, Visual Communications and Image Processing '99; (1998) https://doi.org/10.1117/12.334620
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

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.

Citation Download Citation

Patrick Lechat, Nathalie Laurent, and Henri Sanson "Scalable image coding with fine granularity based on hierarchical mesh", Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); https://doi.org/10.1117/12.334620

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