High-throughput parallel DWT hardware architecture implemented on an FPGA-based platform
- 136 Downloads
To detect fast myoclonus jerks, doctors require a full-HD video capture of the patient at 100 frames per second. Real-time video compression becomes mandatory to archive/transmit the generated data. To achieve this goal, we used a certified medical imaging coder based on discrete wavelet transform (DWT). Thus, a major challenge was to design a 2D DWT architecture, achieving the throughput of 100 full-HD frames/s. The novel unified 2D DWT computation architecture performs both horizontal and vertical transform simultaneously and eliminates the problem of column-wise image pixel accesses to/from the off-chip DDR RAM. All of these factors have led to the reduction of the required off-chip DDR RAM bandwidth by more than 2X. The proposed concept uses four-port line buffers leading to pipelined parallel processing of direct memory access (DMA) read, horizontal 1D DWT, vertical 1D DWT, and DMA write. The proposed architecture has cycles per pixel of just 1/8, making it far exceeds 100 full-HD fps and well positioned for the 4K and 8K video processing. Finally, we highlighted that the developed architecture is highly scalable, outperforms state of the art and is deployed in a first video EEG medical prototype.
KeywordsDWT Lifting scheme FPGA Medical application DDR access
This paper is based upon the work in the Smart EEG project. This project is conducted jointly with the companies CIRA, Partelec, ACACIA, 2CSI, the hospitals Georges Pompidou and Lariboisiere, ETIS and LIP6 labs. The authors thank all the project members for their constructive debate.
- 1.Lambert, L., Despatin, J., Dhif, I., Mhedhbi, I., Ibraheem, M.S., Syed-Zahid, A., Granado, B., Hachicha, K., Pinna, A., Garda, P., Kaddouh, F., Terosiet, M., Histace, A., Romain, O., Bellet, C., Durand, F., Commes, J.P., Hochberg, S., Heudes, D., Lozeron, P., Kubis, N.: Telemedecine, electroencephalography and current issues. SMART-EEG: an innovative solution. Eur. Res. Telemed. 4(3), 81–86 (2015)CrossRefGoogle Scholar
- 3.Mhedhbi, I., Kaddouh, F., Hachicha, K., Heudes, D., Hochberg, S., Garda, P.: Mask motion adaptive medical image coding. In: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 408–411 (2014)Google Scholar
- 4.Ysitcom.: Waaves compression format (2017) http://en.ysitcom.com/
- 5.Philippe, H.S.: Procede de decodage d’image et dispositif de mise en oeuvre de ce procede. WO Patent App. PCT/FR2004/001,942 (2005)Google Scholar
- 7.Lambert, L., Hachicha, K., Ahmed, S.Z., Pinna, A., Garda, P.: Synchronizing physiological data and video in a telemedicine application: a multimedia approach. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 181–185 (2015)Google Scholar
- 21.Darji, A.D., Kushwah, S.S., Merchant, S.N., Chandorkar, A.N.: High-performance hardware architectures for multi-level lifting-based discrete wavelet transform. EURASIP J. Image Video Process. 1, 1–19 (2014)Google Scholar
- 25.Mathworks.: Matlab (2017) https://www.mathworks.com/
- 28.Bai, Y., Ahmed, S.Z., Granado, B.: FPGA implementation of hierarchical enumerative coding for locally stationary image source. In: 2013 23rd International Conference on Field programmable Logic and Applications, pp 1–6 (2013)Google Scholar