Lifting-based fast and low memory DWT computation for IoT platform
Mohd Tausif, Ekram Khan, Mohd Hasan · 2019
The segmented fractional wavelet filter (SFrWF) allows the use of the fractional wavelet filter (FrWF) on line segments, thus can easily be implemented on low-memory devices and sensor nodes to compute the discrete wavelet transform of images. However, the complexity of SFrWF is relatively high due to the use of overlap and add method used in it for avoiding the boundary discontinuities at the segment boundaries. The high complexity of SFrWF makes it unsuitable for low-cost visual sensors used in wireless visual sensor networks (WVSNs)/Internet of things (IoT). In this paper, a lifting-based implementation of SFrWF with 9/7 filter-bank is presented with the aim to reduce it's computational complexity. The proposed lifting based implementation requires fewer computations than SFrWF. Furthermore, the proposed implementation uses an alternative approach to avoid the border discontinuities at segment boundaries. Evaluation results show that for high-resolution ( 2048×2048) images, the complexity of proposed implementation is approximately 40% less than that of SFrWF, without any additional memory requirement.