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Image coding with geometric wavelets Dror Alani*, Amir Averbuch* and Shai Dekel**

Summary: Image coding with geometric wavelets
Dror Alani*, Amir Averbuch* and Shai Dekel**
*School of Computer Science
Tel Aviv University
Tel Aviv 69978, Israel
**GE Healthcare
6 Hamasger St.
Or-Yehuda 60408, Israel
This paper describes a new and efficient method for low bit-rate image coding which is based on
recent development in the theory of multivariate nonlinear piecewise polynomial approximation. It
combines a Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation
so as to efficiently capture curve singularities and provide a sparse representation of the image. The
GW method successfully competes with state-of-the-art wavelet methods such as the EZW, SPIHT
and EBCOT algorithms. We report a gain of about 0.4 dB over the SPIHT and EBCOT algorithms
at the bit-rate 0.0625 bits-per-pixels (bpp). It also outperforms other recent methods that are based
on `sparse geometric representation'. For example, we report a gain of 0.27 dB over the Bandelets
algorithm at 0.1 bpp. Although the algorithm is computationally intensive, its time complexity
can be significantely reduced by collecting a `global' GW n-term approximation to the image from
a collection of GW trees, each constructed separately over tiles of the image.


Source: Averbuch, Amir - School of Computer Science, Tel Aviv University


Collections: Computer Technologies and Information Sciences