SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders

Mohd Tausif, Ekram Khan, Mohd Hasan, Martin Reisslein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The Discrete Wavelet Transform (DWT) is extensively used for image coding due to its excellent energy compaction property and its ability to simultaneously analyze images in space-frequency domains. However, conventional methods of computing the DWT coefficients of an image require large amounts of memory, thus making them unsuitable for memory-constraint low-cost portable devices. In this paper we propose a novel low memory approach named Segmented Fractional Wavelet Filter SFrWF to compute the DWT of high resolution images on low-memory devices. Evaluation results show that the SFrWF requires less than 10 kB of RAM for a gray-scale image of size 2048×2048 thus making the SFrWF suitable for low-cost visual sensor nodes.

Original languageEnglish (US)
Title of host publication2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages593-597
Number of pages5
ISBN (Electronic)9781538630044
DOIs
StatePublished - Jun 28 2017
Event4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017 - Mathura, India
Duration: Oct 26 2017Oct 28 2017

Publication series

Name2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017
Volume2018-January

Other

Other4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017
Country/TerritoryIndia
CityMathura
Period10/26/1710/28/17

Keywords

  • Discrete Wavelet Transform
  • Fractional Wavelet Filter
  • Low memory
  • Visual sensor nodes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation
  • Computer Science Applications

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