Exploration and Development of the JPEG Compression for Mobile Communications System

Exploration and Development of the JPEG Compression for Mobile Communications System

Andik Setyono, Md. Jahangir Alam, C. Eswaran
DOI: 10.4018/jmcmc.2013010103
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The JPEG compression algorithm is widely used in communications technology for multimedia data transmission. This algorithm is also very efficient for mobile applications since it can achieve compression ratios more than 100:1, thus greatly facilitating the storage and transmission processes for images. Though lossless JPEG compression is an ideal solution, the compression ratio achieved with this technique is relatively very small. JPEG2000 provides higher compression ratio and quality compared to JPEG but the main problem with this compression technique is its complexity resulting in longer processing time thus making it unsuitable for mobile communications. In this study, the authors explore methods for enhancing the performance of JPEG compression standard for mobile applications. They show that by using a splitting technique along with the JPEG compression, one can transmit data files of size larger than the maximum capacity which is possible with the existing mobile network. To evaluate the performance of proposed method, the authors perform some simulations using the emulator on desktop computer and mobile phone. The parameters used for performance evaluation are the speed of the compression process, the compression ratio and the compressed image quality. The simulation results presented in this paper will be very useful for developing a practical mobile communication system for multimedia data using JPEG compression.
Article Preview
Top

1. Introduction

For storing large size files in limited capacity memories, they need to be compressed using appropriate data compression techniques (Rafael & Richard, 2001). Image compression is an important topic in the digital world. A bitmap image can contain considerably large amounts of data causing some problems in both computational complexity as well as data processing. Compression is important to manage large amounts of data for network, internet, or storage media (Santa, 2006). With small size, the compressed image will be easily transmitted in a network that has bandwidth limitation such as mobile communications network. There are two types of data compression, namely, lossy and lossless compression. Lossy compression is a compression technique in which the original data cannot be reconstructed back (data is lost). Lossy Compression aims to streamline the data which is usually used to compress multimedia data. Lossless compression is a compression technique that does not alter the original data information. Lossless compression does not cause data loss. This technique is suitable to be used to compress text data and programs (David, 2004). Joint Photographic Experts Group (JPEG) compression is a lossy compression which can be used with advantage for mobile communications (Tom, 2010; Yun & Huifang, 2000).

The JPEG algorithm is an image compression algorithm that has been developed and applied to information and multimedia communications technology (David, 2004). The performance results show that the JPEG algorithm can be successfully used on all software that support the information technology in photo and digital video cameras (Edi et al., 2010). In this paper, we focus on JPEG compression because this format is used widely and has already become a default format for digital images. Actually, JPEG2000 yields better compression ratio and image quality than the JPEG baseline standard (Rosenbaum, 2006; Medouakh, 2011). However this format is not very popular because it is expensive and it is also complex causing lengthy time process. JPEG2000 as well as lossless JPEG are not suitable to be implemented on the mobile phone for mobile communications, since mobile devices have shortcomings with respect to memory, display and processing power to perform these compression algorithms.

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024)
Volume 14: 1 Issue (2023)
Volume 13: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing