Open AccessCOMPARATIVE ANALYSIS OF COMPACT METHODS REPRESENTATIONS OF GRAPHIC INFORMATION
Author(s) -
O. Gertsiy
Publication year - 2021
Publication title -
zbìrnik naukovih pracʹ deržavnogo unìversitetu ìnfrastrukturi ta tehnologìj. serìâ "transportnì sistemi ì tehnologìï"
Language(s) - English
Resource type - Journals
eISSN - 2617-9059
pISSN - 2617-9040
DOI - 10.32703/2617-9040-2021-37-13
Subject(s) - jpeg , computer science , lossless compression , huffman coding , task (project management) , jpeg 2000 , data compression , image compression , file size , transmission (telecommunications) , lossy compression , relevance (law) , data transmission , computer graphics (images) , computer vision , computer hardware , image processing , artificial intelligence , image (mathematics) , telecommunications , management , political science , law , economics , operating system
The main characteristics of graphic information compression methods with losses and without losses (RLE, LZW, Huffman's method, DEFLATE, JBIG, JPEG, JPEG 2000, Lossless JPEG, fractal and Wawelet) are analyzed in the article. Effective transmission and storage of images in railway communication systems is an important task now. Because large images require large storage resources. This task has become very important in recent years, as the problems of information transmission by telecommunication channels of the transport infrastructure have become urgent. There is also a great need for video conferencing, where the task is to effectively compress video data - because the greater the amount of data, the greater the cost of transmitting information, respectively. Therefore, the use of image compression methods that reduce the file size is the solution to this task. The study highlights the advantages and disadvantages of compression methods. The comparative analysis the basic possibilities of compression methods of graphic information is carried out. The relevance lies in the efficient transfer and storage of graphical information, as big data requires large resources for storage. The practical significance lies in solving the problem of effectively reducing the data size by applying known compression methods.