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.
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