A contour-based approach to image compression

Scarmana, Gabriel (2011) A contour-based approach to image compression. In: 2011 International Conference on Digital Image Computing: Techniques and Application (DICTA 2011), 6-8 Dec 2011, Noosa, Australia .


This paper presents an alternative spatial image compression method that can be directly applied to
compressing certain classes of grey-scale and/or colour (RGB, 24 bits) imagery of any size. The process involves the
vectorisation of digital images into contour maps with
subsequent converting of the contours to a pixel format.
Contours are often approximated by polygons (linear or
otherwise), and only the vertices of the polygons need to be
retained to recreate the original contour lines. The final
amount of storage depends crucially on the complexity of the
image and on the degree of approximation desired.
In this context, the compression process is based on filtering and eliminating those contours that may contain redundant information. Contours extracted from digital images may contain multiple redundant data (i.e. intersecting or nested contours), any of which might logically be used as a basis for discrimination and/or used in the reconstruction of an original captured image.
Depending on the applications, and the amount of contour lines employed in the reconstruction of an image, the process allows for various levels of image accuracy while preserving visual integrity and reducing compression artefacts.
The proposed method does not require special hardware and,
if combined with existing encoding schemes, can be efficiently used for image transmission purposes.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Date Deposited: 23 May 2012 04:45
Last Modified: 28 Aug 2014 06:00
Uncontrolled Keywords: contouring; image compression; image reconstruction; image processing; image editing
Fields of Research (2008): 09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010303 Optimisation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing
49 MATHEMATICAL SCIENCES > 4903 Numerical and computational mathematics > 490304 Optimisation
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: https://doi.org/10.1109/DICTA.2011.38
URI: http://eprints.usq.edu.au/id/eprint/20382

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