Image Data Compression in the Public Reporting System in Lamongan using the Huffman Method and Run Length Encoding
- 10.2991/assehr.k.220301.146How to use a DOI?
- public reporting; image compression; RLE; Huffman
The dry season and the rainy season in the Lamongan area always cause several problems, including water scarcity, flooding, damaged roads, etc. The public report site, which is used to accommodate the aspirations of Lamongan residents who are in trouble, requires image evidence to ensure that there are no false reports. The site has a limit on the size of the file to be uploaded, so if the file size is too large, the upload process cannot be carried out. In this study, an analysis will be carried out to compare what compression method produces photos with the smallest size for further upload on the Lamongan community report site. Huffman compression and Run Length Encoding (RLE) were chosen because the algorithm includes lossless compression where the compressed image will not be damaged even though the size is compressed. From the two methods or algorithms, testing is carried out to find out which algorithm is the best that can produce compressed images with the smallest size. From the experiments conducted, it is known that the RLE method is a better method than Huffman coding. With the RLE method we can compress images up to 93.17%.
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Mohammad Robihul Mufid AU - Saniyatul Mawaddah AU - Arif Basofi AU - Mochammad Jauhar Ulul Albab AU - Nur Syaela Majid AU - Devvana Arya Pratama AU - Risalatun Nuriyah AU - Nidalifa Choirunnisa PY - 2022 DA - 2022/03/04 TI - Image Data Compression in the Public Reporting System in Lamongan using the Huffman Method and Run Length Encoding BT - Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021) PB - Atlantis Press SP - 887 EP - 891 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220301.146 DO - 10.2991/assehr.k.220301.146 ID - Mufid2022 ER -