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.
| Published in | Science Journal of Circuits, Systems and Signal Processing (Volume 7, Issue 4) |
| DOI | 10.11648/j.cssp.20180704.11 |
| Page(s) | 81-86 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Arduino Uno, Raspberry Pi, Image Compression, Vector Quantization, Embedded System
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APA Style
Aurelle Tchagna Kouanou, Daniel Tchiotsop, Theophile Fonzin Fozin, Bayangmbe Mounmo, René Tchinda. (2019). Real-Time Image Compression System Using an Embedded Board. Science Journal of Circuits, Systems and Signal Processing, 7(4), 81-86. https://doi.org/10.11648/j.cssp.20180704.11
ACS Style
Aurelle Tchagna Kouanou; Daniel Tchiotsop; Theophile Fonzin Fozin; Bayangmbe Mounmo; René Tchinda. Real-Time Image Compression System Using an Embedded Board. Sci. J. Circuits Syst. Signal Process. 2019, 7(4), 81-86. doi: 10.11648/j.cssp.20180704.11
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Download@article{10.11648/j.cssp.20180704.11,
author = {Aurelle Tchagna Kouanou and Daniel Tchiotsop and Theophile Fonzin Fozin and Bayangmbe Mounmo and René Tchinda},
title = {Real-Time Image Compression System Using an Embedded Board},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {7},
number = {4},
pages = {81-86},
doi = {10.11648/j.cssp.20180704.11},
url = {https://doi.org/10.11648/j.cssp.20180704.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20180704.11},
abstract = {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.},
year = {2019}
}
TY - JOUR T1 - Real-Time Image Compression System Using an Embedded Board AU - Aurelle Tchagna Kouanou AU - Daniel Tchiotsop AU - Theophile Fonzin Fozin AU - Bayangmbe Mounmo AU - René Tchinda Y1 - 2019/03/27 PY - 2019 N1 - https://doi.org/10.11648/j.cssp.20180704.11 DO - 10.11648/j.cssp.20180704.11 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 81 EP - 86 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.20180704.11 AB - 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. VL - 7 IS - 4 ER -
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