@inproceedings{94d4597022284a199ccf13ed5593d526,
title = "SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders",
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.",
keywords = "Discrete Wavelet Transform, Fractional Wavelet Filter, Low memory, Visual sensor nodes",
author = "Mohd Tausif and Ekram Khan and Mohd Hasan and Martin Reisslein",
year = "2017",
month = jun,
day = "28",
doi = "10.1109/UPCON.2017.8251116",
language = "English (US)",
series = "2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "593--597",
booktitle = "2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017",
note = "4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017 ; Conference date: 26-10-2017 Through 28-10-2017",
}