Lifting-based fast and low memory DWT computation for IoT platform

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 ($2048\times 2048$) images, the complexity of proposed implementation is approximately 40% less than that of SFrWF, without any additional memory requirement.

[1]  Mrityunjaya V. Latte,et al.  Reduced memory listless speck image compression , 2006, Digit. Signal Process..

[2]  Martin Reisslein,et al.  ZM-SPECK: A Fast and Memoryless Image Coder for Multimedia Sensor Networks , 2016, IEEE Sensors Journal.

[3]  Martin Reisslein,et al.  Performance evaluation of the fractional wavelet filter: A low-memory image wavelet transform for multimedia sensor networks , 2011, Ad Hoc Networks.

[4]  Martin Reisslein,et al.  Low-Memory Wavelet Transforms for Wireless Sensor Networks: A Tutorial , 2011, IEEE Communications Surveys & Tutorials.

[5]  Byung-Seo Kim,et al.  Internet of Things (IoT) Operating Systems Support, Networking Technologies, Applications, and Challenges: A Comparative Review , 2018, IEEE Communications Surveys & Tutorials.

[6]  Karolin Baecker,et al.  Two Dimensional Signal And Image Processing , 2016 .

[7]  Mohd Hasan,et al.  BFrWF: Block-based FrWF for coding of high-resolution images with memory-complexity constrained -devices , 2018, 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON).

[8]  Imran Baig,et al.  Wireless Sensor’s Civil Applications, Prototypes, and Future Integration Possibilities: A Review , 2018, IEEE Sensors Journal.

[9]  Chih-Hsien Hsia,et al.  Memory-Efficient Hardware Architecture of 2-D Dual-Mode Lifting-Based Discrete Wavelet Transform , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Mohd Hasan,et al.  SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders , 2017, 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON).

[11]  Jayashree,et al.  IoT based Smart Village , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[12]  Xiangjian He,et al.  SAMS: A Seamless and Authorized Multimedia Streaming Framework for WMSN-Based IoMT , 2019, IEEE Internet of Things Journal.

[13]  Zujun Hou,et al.  Memory Efficient Multilevel Discrete Wavelet Transform Schemes for JPEG2000 , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  William A. Pearlman,et al.  SPIHT image compression without lists , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[15]  C.-C. Jay Kuo,et al.  Design of wavelet-based image codec in memory-constrained environment , 2001, IEEE Trans. Circuits Syst. Video Technol..

[16]  Martin Reisslein,et al.  SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT) , 2019, IEEE Access.

[17]  Manuel P. Malumbres,et al.  On the Design of Fast Wavelet Transform Algorithms With Low Memory Requirements , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Li Wern Chew,et al.  Low memory image stitching and compression for WMSN using strip-based processing , 2012, Int. J. Sens. Networks.

[19]  Kah Phooi Seng,et al.  New Virtual SPIHT Tree Structures for Very Low Memory Strip-Based Image Compression , 2008, IEEE Signal Processing Letters.

[20]  Fernando J. Velez,et al.  Survey on the Characterization and Classification of Wireless Sensor Network Applications , 2014, IEEE Communications Surveys & Tutorials.

[21]  Hyuk-Jae Lee,et al.  A Low-Cost Hardware Design of a 1-D SPIHT Algorithm for Video Display Systems , 2018, IEEE Transactions on Consumer Electronics.

[22]  Sunanda Mitra,et al.  Low-memory-usage image coding with line-based wavelet transform , 2011 .