Science Journal of Circuits, Systems and Signal Processing
Volume 7, Issue 4, December 2018, Pages: 81-86
Received: Jan. 26, 2019;
Accepted: Mar. 7, 2019;
Published: Mar. 27, 2019
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Aurelle Tchagna Kouanou, Research Unity of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, Dschang, Cameroon
Daniel Tchiotsop, Research Unity of Automatic and Applied Informatic, IUT-FV of Bandjoun, University of Dschang-Cameroun, Cameroun, Bandjoun
Theophile Fonzin Fozin, Research Unity of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, Dschang, Cameroon
Bayangmbe Mounmo, National Advanced School of Posts, Telecommunications and Information Communication and Technologies (SUP'PTIC), Yaounde, Cameroon
René Tchinda, Research Unity of Engineering Industrial Systems and Environments, University of Dschang, Cameroun, Bandjoun
Digital image processing is the use of computer algorithms to improve image quality, to extract or add information. Image compression is a part of image processing and is used to reduce the quantity of data to store. This paper presents the implementation of image compression operations on low cost embedded systems (Raspberry Pi 3, Arduino Uno R3). Motivated by the work of Tchagna et al. (DOI: 10.5815/ijigsp.2018.11.05), we proposed within this paper a real-time implementation of the compression algorithm on embedded boards. Our investigation in this paper is to make a real-time compression system able to capture an image, apply compression algorithm and save compression image on an SD card (for Arduino or Raspberry) or sent directly compressed image to cloud. Compression system with a Raspberry Pi basically used a webcam USB camera to capture the images, the compression function based on python language, and a function to store compressed image to an SD card or to Cloud. The compression system with Arduino used an SD card where the image to be compressed are stored, an external SRAM chip, and an Ethernet shield. The proposed hardware system can decompress the image. In opposition to the approach adopted in the literature, all the results presented within this work use the vector quantization. Eight images have been used to evaluate and compared the compression time for each board according to codebook size used during vector quantization step. Based on our results, we remark that compression and decompression time using Raspberry Pi is lower than compression and decompression time using Arduino. Raspberry Pi offers many possibilities and its processor is bigger than Arduino processor. This justifies the obtained compression and decompression time using Raspberry Pi compared to those with Arduino.
Aurelle Tchagna Kouanou,
Theophile Fonzin Fozin,
Real-Time Image Compression System Using an Embedded Board, Science Journal of Circuits, Systems and Signal Processing.
Vol. 7, No. 4,
2018, pp. 81-86.
Tchagna KA, Tchiotsop D, Tchinda R, Tchapga CT, Kengnou Telem AN, Kengne R, A (2018) Machine Learning Algorithm for Biomedical Images Compression Using Orthogonal Transforms, I. J. Image, Graphics and Signal Processing 11, 38-53. DOI: 10.5815/ijigsp.2018.11.05.
Kar S, Jana A, Chatterjee D, Mitra D, Banerjee S, Kundu D, Chosh S, Gupta SD (2015), Image Processing Based Customized Image Editor and Gesture Controlled Embedded Robot Coupled with Voice Control Features, (IJACSA) Int. J. of Advanced Computer Science and Applications 6 (11), 91-96.
Molina-Cantero AJ, Castro-Garcia JA, Lebrato-Vasquez C, Gomez-Gonzalez IM and Merino-Monge M (2018) Real-Time Processing Library for Open-Source Hardware Biomedical Sensors, Sensors 18, 1033; doi:10.3390/s18041033.
Vostrukhin A and Vakhtina E, Studying Digital Signal Processing on Arduino Based Platform (2016), Engineering for Rural Development, 236-241.
Sieczkowski K and Sondej T (2017) Use of a Raspberry PI single-board computer for image acquisition and transmission, Measurement Automation Monitoring 63 (5), 180-182.
Lazar J, Kostolanyova K, Bradac V (2017) Processing and image compression based on the platform Arduino, AIP Conference Proceedings 1863, 070025. http://dx.doi.org/10.1063/1.4992247.
Sahitya S, Lokesha H, Sudha LK, Real Time Application of Raspberry Pi in Compression of Images, IEEE International Conference on Recent Trends in Electronics Information Communication Technology, May 20-21, 2016, India. 978-1-5090-0774-5/16/$31.00.
Sushma G, Joseph M, Tabitha AR, Yokesh MBP (2015) Image Tracking Based Home Security Using Arduino Microcontroller, Int. J. of Innovative Research in Computer and Communication Engineering 3 (8), 117-122.
Shilpashree KS, Lokesha H, Shivkumar H (2015), Implementation of Image Processing on Raspberry Pi, Int. J. of Advanced Research in Computer and Communication Engineering, 4 (6), 199-202. DOI 10.17148/IJARCCE.2015.4545.
Yan Z, Che X, Walker J, Remote Image Sensing Platform Based on Arduino, IEEE 6th Computer Science and Electronic Engineering Conference, 25-26 Sept, 2014, Colchester. 10.1109/CEEC.2014.6958550.
Tchapga Tchito C, Tchiotsop D, Fomethe A, NodeMCU in Patient’s Data Transfer to IoT Platform (2018) Journal of Biomedical Engineering and Medical Imaging 5 (3), 9-18, DOI: 10.14738/jbemi.53.4358.
Shah D and Haradi V (2016) IoT Based Biometrics Implementation on Raspberry Pi, Procedia Computer Science 79, 328 – 336. doi: 10.1016/j.procs.2016.03.043.
Jamieson P and Herdtner J, More missing the Boat—Arduino, Raspberry Pi, and small prototyping boards and engineering education needs them. In Proceedings of the Frontiers in Education Conference (FIE), Washington, 21–24 Oct, 2015 1–6.
Al-Ani MS, Hardware Implementation of a Real Time Image Compression (2017) IOSR Journal of Computer Engineering (IOSR-JCE) 19 (3), 6-13. DOI: 10.9790/0661-1903050613.
Kumar Shiva NR and Venkatesh Murthy NK (2016), Design and Implementation of Novel Video Compression Technique Using Raspberry Pi, Int. J. of Advanced Research in Electrical, Electronics and Instrumentation Engineering 5(4), 3400-3404. DOI:10.15662/IJAREEIE.2016.0504175.
Sayood K, Introduction to Data Compression. Morgan Kaufmann, San Francisco, 2006.
Salomon D, A Concise Introduction to data compression, Springer-Verlag, London, 2008.
Fitzgerald, Shiloh M, Igoe T, The Arduino Projects Book, Arduino LLC, Torino, 2012.
Hughes JM, Arduino: A Technical Reference, O’Reilly Media, Sebastopol, 2016.
Halfacree G, Raspberry Pi: Beginner’s Guide, Raspberry Pi Trading Ltd, Cambridge, 2018.
Tchagna KA, Tchiotsop D, Kengne R, Djoufack ZT, Ngo Mouelas AA, Tchinda R (2018) An Optimal Big Data Workflow for Biomedical Image Analysis, Informatic in Medicine Unlocked 11, 68–74. https://doi.org/10.1016/j.imu.2018.05.001.
Kouanou AT, Tchiotsop D, Tchinda R, Tansaa ZD (2019) A Machine Learning Algorithm for Image Compression with application to Big Data Architecture: A Comparative Study. Br Biomed Bull Vol.7 No.1:316.