FrWF-based LMBTC: Memory-efficient image coding for visual sensors

Mohd Tausif, Naimur Rahman Kidwai, Ekram Khan, Martin Reisslein

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

After the successful development of JPEG2000, many state-of-the-art wavelet-based image coding algorithms have been developed. However, the traditional discrete wavelet transform (DWT) is implemented with memory intensive and time-consuming algorithms and, therefore, has very high system resource requirements. In particular, the very large requirement of memory poses a serious limitation for multimedia applications on memory-constrained portable devices, such as digital cameras and sensor nodes. In this paper, we propose a novel wavelet-based image coder with low memory requirements and low complexity that preserves the compression efficiency. Our encoder employs the fractional wavelet filter (FrWF) to calculate the DWT coefficients, which are quantized and encoded with a novel low memory block tree coding (LMBTC) algorithm. The LMBTC is a listless form of the wavelet block tree coding algorithm. Simulation results demonstrate that the proposed coder significantly reduces memory requirements and computational complexity and has competitive coding efficiency in comparison with other state-of-the-art coders. The FrWF combined with the LMBTC is, thus, a viable option for image communication over wireless sensor networks.

Original languageEnglish (US)
Article number7156064
Pages (from-to)6218-6228
Number of pages11
JournalIEEE Sensors Journal
Volume15
Issue number11
DOIs
StatePublished - Nov 1 2015

Fingerprint

Image coding
coding
filters
Data storage equipment
sensors
Sensors
coders
requirements
Discrete wavelet transforms
wavelet analysis
digital cameras
Digital cameras
multimedia
Sensor nodes
Wireless sensor networks
Computational complexity
resources
communication
Communication
coefficients

Keywords

  • Fractional wavelet filter
  • Low memory image codec
  • Visual sensors
  • Wireless sensor networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

Cite this

FrWF-based LMBTC : Memory-efficient image coding for visual sensors. / Tausif, Mohd; Kidwai, Naimur Rahman; Khan, Ekram; Reisslein, Martin.

In: IEEE Sensors Journal, Vol. 15, No. 11, 7156064, 01.11.2015, p. 6218-6228.

Research output: Contribution to journalArticle

Tausif, Mohd ; Kidwai, Naimur Rahman ; Khan, Ekram ; Reisslein, Martin. / FrWF-based LMBTC : Memory-efficient image coding for visual sensors. In: IEEE Sensors Journal. 2015 ; Vol. 15, No. 11. pp. 6218-6228.
@article{301ca9af913d48ad86a1e4229e84d567,
title = "FrWF-based LMBTC: Memory-efficient image coding for visual sensors",
abstract = "After the successful development of JPEG2000, many state-of-the-art wavelet-based image coding algorithms have been developed. However, the traditional discrete wavelet transform (DWT) is implemented with memory intensive and time-consuming algorithms and, therefore, has very high system resource requirements. In particular, the very large requirement of memory poses a serious limitation for multimedia applications on memory-constrained portable devices, such as digital cameras and sensor nodes. In this paper, we propose a novel wavelet-based image coder with low memory requirements and low complexity that preserves the compression efficiency. Our encoder employs the fractional wavelet filter (FrWF) to calculate the DWT coefficients, which are quantized and encoded with a novel low memory block tree coding (LMBTC) algorithm. The LMBTC is a listless form of the wavelet block tree coding algorithm. Simulation results demonstrate that the proposed coder significantly reduces memory requirements and computational complexity and has competitive coding efficiency in comparison with other state-of-the-art coders. The FrWF combined with the LMBTC is, thus, a viable option for image communication over wireless sensor networks.",
keywords = "Fractional wavelet filter, Low memory image codec, Visual sensors, Wireless sensor networks",
author = "Mohd Tausif and Kidwai, {Naimur Rahman} and Ekram Khan and Martin Reisslein",
year = "2015",
month = "11",
day = "1",
doi = "10.1109/JSEN.2015.2456332",
language = "English (US)",
volume = "15",
pages = "6218--6228",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",

}

TY - JOUR

T1 - FrWF-based LMBTC

T2 - Memory-efficient image coding for visual sensors

AU - Tausif, Mohd

AU - Kidwai, Naimur Rahman

AU - Khan, Ekram

AU - Reisslein, Martin

PY - 2015/11/1

Y1 - 2015/11/1

N2 - After the successful development of JPEG2000, many state-of-the-art wavelet-based image coding algorithms have been developed. However, the traditional discrete wavelet transform (DWT) is implemented with memory intensive and time-consuming algorithms and, therefore, has very high system resource requirements. In particular, the very large requirement of memory poses a serious limitation for multimedia applications on memory-constrained portable devices, such as digital cameras and sensor nodes. In this paper, we propose a novel wavelet-based image coder with low memory requirements and low complexity that preserves the compression efficiency. Our encoder employs the fractional wavelet filter (FrWF) to calculate the DWT coefficients, which are quantized and encoded with a novel low memory block tree coding (LMBTC) algorithm. The LMBTC is a listless form of the wavelet block tree coding algorithm. Simulation results demonstrate that the proposed coder significantly reduces memory requirements and computational complexity and has competitive coding efficiency in comparison with other state-of-the-art coders. The FrWF combined with the LMBTC is, thus, a viable option for image communication over wireless sensor networks.

AB - After the successful development of JPEG2000, many state-of-the-art wavelet-based image coding algorithms have been developed. However, the traditional discrete wavelet transform (DWT) is implemented with memory intensive and time-consuming algorithms and, therefore, has very high system resource requirements. In particular, the very large requirement of memory poses a serious limitation for multimedia applications on memory-constrained portable devices, such as digital cameras and sensor nodes. In this paper, we propose a novel wavelet-based image coder with low memory requirements and low complexity that preserves the compression efficiency. Our encoder employs the fractional wavelet filter (FrWF) to calculate the DWT coefficients, which are quantized and encoded with a novel low memory block tree coding (LMBTC) algorithm. The LMBTC is a listless form of the wavelet block tree coding algorithm. Simulation results demonstrate that the proposed coder significantly reduces memory requirements and computational complexity and has competitive coding efficiency in comparison with other state-of-the-art coders. The FrWF combined with the LMBTC is, thus, a viable option for image communication over wireless sensor networks.

KW - Fractional wavelet filter

KW - Low memory image codec

KW - Visual sensors

KW - Wireless sensor networks

UR - http://www.scopus.com/inward/record.url?scp=84941661509&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941661509&partnerID=8YFLogxK

U2 - 10.1109/JSEN.2015.2456332

DO - 10.1109/JSEN.2015.2456332

M3 - Article

AN - SCOPUS:84941661509

VL - 15

SP - 6218

EP - 6228

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 11

M1 - 7156064

ER -