WolfgangErb / GraphWedgelets

Graph wedgelets for image compression

Graph Wedgelets for Image Compression

A simple toolbox to illustrate how graph wedgelets can be used to sparsely approximate and compress images

Fig. 1: Wedgelet compression of images. a) original image with 481 x 321 pixels; b)c)d) BWP compression with 1000, 500 and 100 graph wedgelets; e)f)g) center nodes for BWP encoding in b)c)d). The PSNR values are b) 40.762 dB, c) 37.935 dB, and d) 31.827 dB.

Version: 0.1 (01.10.2021)

Written by Wolfgang Erb

General Description

This package contains a Matlab implementation for the illustration of graph-based wedgelets in image approximation and compression.

Graph wedgelets are a tool for lossy data compression based on the approximation of graph signals by piecewise constant functions on adaptively generated binary wedge partitioning trees (BWP trees) on a graph. Graph wedgelets are discrete variants of continuous wedgelets and binary space partitionings known from image processing. Wedgelet representations of graph signals can be encoded in a simple way by a set of graph nodes and applied easily to the compression of graph signals and images. A detailed description of the encoding and decoding of graph signals with wedgelets is given in [1].

Fig. 2: Wedgelet compression of images. a) original image with 481 x 321 pixels; b)c) FA-greedy BWP compression with 2000 and 1000 nodes; d) wavelet details between b) and c); e)f) MD-greedy BWP compression with 2000 and 1000 nodes; g) wavelet details between e) and f).

Description of the Code

The package contains three main parts

• The main folder contains three example scripts on how to calculate and apply the wedgelet decomposition for images. The package works for RGB images as well as for gray-scale images.

• The subfolder ./core contains the core code of the package for wedgelet encoding and decoding of images.

• The subfolder ./data contains two example images from the BSDS500 dataset.

Remarks

This code is written for educational purposes and is not optimized for speed nor for optimal data storage.

Citation and Credits

This code was written by Wolfgang Erb at the Dipartimento di Matematica ''Tullio Levi-Civita'', University of Padova. The corresponding theory related to graph wedgelets and data compression can be found in

• [1]   Erb, W.
  Graph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets. arXiv:2110.08843 [eess.SP] (2021)

Source for the two original images: Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)

License

Copyright (C) 2021 Wolfgang Erb

GraphWedgelets is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.