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Telecommunications and Radio Engineering

年間 12 号発行

ISSN 印刷: 0040-2508

ISSN オンライン: 1943-6009

SJR: 0.185 SNIP: 0.268 CiteScore™:: 1.5 H-Index: 22

Indexed in

MULTI-LEVEL WAVELET BASED IMAGE CODING OVER LIFTING SCHEME FOR EMBEDDED WIRELESS DEVICES

巻 78, 発行 15, 2019, pp. 1345-1353
DOI: 10.1615/TelecomRadEng.v78.i15.40
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要約

This paper provides a concise synopsis in the application of Lifting Scheme (LS) in wavelet image coding. Lifting theorem was applied via LS 5/3 wavelet transform to develop a design wherein multipliers are replaced with shifters, thus lowering the volume of operations entailed in the process of computing a DWT to approximately one-half of the requisite volumes of a convolution approach. Consequently, the computations required for image coding are reduced and less intricate. In addition, the lifting scheme can be modified to meet the demands of in-place computation, for DWT to be implemented in low memory systems, which presents a unique solution to the issues associated with existing time consuming software algorithms. This LS filter consists of integer adder units and binary shifter instead of multiplier and divider units found in convolution based filters; thus it has been modified to provide energy efficient hardware performance. The low power 5/3 LS based wavelet transform involves solutions developed to resolve the energy and bandwidth communication problems related to image data transmission. These solutions entail constructing 2D-DWT image codec architecture to save the computational and communication energy erstwhile dissipated in existing architectures.

参考
  1. Jain, N., Singh, M., and Mishra, B., (2018) Image Compression Using 2D-Discrete Wavelet Transform on a Light Weight Reconfigurable Hardware, 17th International Conference on Embedded Systems (VLSID), 2018 31st International Conference on, pp. 61-66, IEEE.

  2. Johnsy, A.C. and Schirinzi, G., (2017) A lossless coding scheme for maps using binary wavelet transform, European Journal of Remote Sensing, 50(1), pp.77-86.

  3. Huang, C.T., Tseng, P.C., and Chen, L.G., (2005) Generic RAM-based architectures for two-dimensional discrete wavelet transform with line-based method, IEEE Transactions on Circuits and Systems for Video Technology, 15(7), pp.910-920.

  4. Angelopoulou, M.E., Masselos, K., Cheung, P.Y., and Andreopoulos, Y., (2008) Implementation and comparison of the 5/3 lifting 2D discrete wavelet transform computation schedules on FPGAs, Journal of Signal Processing Systems, 51(1), pp.3-21.

  5. Hasan, K.K., Ngah, U.K., and Salleh, M.F.M., (2014) Efficient hardware-based image compression schemes for wireless sensor networks: A survey, Wireless Personal Communications, 77(2), pp. 1415-1436.

  6. Taubman, D., (2000) High performance scalable image compression with EBCOT, IEEE Transactions on Image Processing, 9(7), pp. 1158-1170.

  7. Tseng, H.-W. and Chang, C.-C., (2005) A very low bit rate image compressor using transformed classified vector quantization, Informatica, 29(3).

  8. Hasan, K.K., Ngah, U.K., and Salleh, M.F.M., (2012) The most proper wavelet filters in low- complexity and an embedded hierarchical image compression structures for wireless sensor network implementation requirements, Control System, Computing and Engineering (ICCSCE), IEEE International Conference on, pp. 137-142.

  9. Hasan, K.K., Ngah, U.K., and Salleh, M.F.M., (2013) Multilevel decomposition discrete wavelet transform for hardware image compression architectures applications, Control System, Computing and Engineering (ICCSCE), IEEE International Conference on, pp. 315-320.

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