Data Compression Techniques

Data compression is very necessary in business data processing, because of the cost savings that it offers and the large volume of data manipulated in many business applications. It is a method or system for transmitting a digital image (i.e., an array of pixels) from a digital data source to a digital data receiver. More the size of the data be smaller, it provides better transmission speed and saves time. In this communication, we always want to transmit data efficiently and noise freely. This paper will provide some compression techniques for loss less text type data compression and comparative result of multiple and single compression, that will help to find out better compression output and to develop compression algorithms.

Data transmission and storage cost money. The more information being dealt with, the more it costs. In spite of this, most digital data are not stored in the most compact form. Rather, they are stored in whatever way makes them easiest to use, such as: ASCII text from word processors, binary code that can be executed on a computer, individual samples from a data acquisition system, etc. Typically, these easy-to-use encoding methods require data files about twice as large as actually needed to represent the information. Data compression is the general term for the various algorithms and programs developed to address this problem. A compression program is used to convert data from an easy-to-use format to one optimized for compactness. Likewise, an uncompression program returns the information to its original form. We examine five techniques for data compression in this chapter. The first three are simple encoding techniques, called: runlength, Huffman, and delta encoding. The last two are elaborate procedures that have established themselves as industry standards: LZW and JPEG.

This research paper provides lossless data compression methodologies and compares their performance. Huffman and arithmetic coding are compare according to their performances. Data compression is a process that reduces the data size, removing the excessive information. Shorter data size is suitable because it simply reduces the cost. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression is important application in the area of file storage and distributed system because in distributed system data have to send from and to all system. So for speed and performance efficiency data compression is used. There are number of different data compression methodologies, which are used to compress different data formats like text, video, audio, image files. There are two forms of data compression “lossy” and “lossless”, in lossless data compression, the integrity of data is preserved.

2016

Compression reduces the number of bits required to represent the data. Compression is useful because it helps in reducing the consumption of expensive resources, such as disk space and transmission bandwidth. Compression is built into a broad range of technologies like storage systems, databases operating systems and software applications. Hence selection of data compression algorithm should be appropriate. This paper presents different data compression methodologies. Mainly there are two forms of data compression :Lossless and Lossy. In this paper, we discussed about some of the Lossless and Lossy data compression methods.

Data compression is the process of reducing the size of a file to effectively reduce storage space and communication cost. The evolvement in technology and digital age has led to an unparalleled usage of digital files in this current decade. The usage of data has resulted to an increase in the amount of data being transmitted via various channels of data communication which has prompted the need to look into the current lossless data compression algorithms to check for their level of effectiveness so as to maximally reduce the bandwidth requirement in communication and transfer of data. Four lossless data compression algorithm: Lempel-Ziv Welch algorithm, Shannon-Fano algorithm, Adaptive Huffman algorithm and Run-Length encoding have been selected for implementation. The choice of these algorithms was based on their similarities, particularly in application areas. Their level of efficiency and effectiveness were evaluated using some set of predefined performance evaluation metrics namely compression ratio, compression factor, compression time, saving percentage, entropy and code efficiency. The algorithms implementation was done in the NetBeans Integrated Development Environment using Java as the programming language. Through the statistical analysis performed using Boxplot and ANOVA and comparison made on the four algorithms, Lempel Ziv Welch algorithm was the most efficient and effective based on the metrics used for evaluation.

This research paper provides lossless data compression techniques and comparison between them. Data Compression is a process which reduces the size of data removing excessive information from it. It reduces the redundancy in data representation to decrease the storage required for that data and thus also reduces the communication cost by using the available bandwidth effectively. Data compression is important application in the area of file storage and distributed system. For different data formats like text, audio, video and image files there are different data compression techniques. Mainly there are two forms of data compression:-Lossy and Lossless. But in the lossless data compression, the integrity of data is to be preserved.

This research paper offers data compression practices and matches their performance with the help of a software .In this research paper we are going to discuss the software which compare three algorithms, This is the software which we are going to compare three compression algorithms Huffman, LZW, LZM .With the help these algorithms we capable to compress text file, audio file, video file and images files. The software is basically comparing the algorithms on the basis of space complexity, time complexity of the data after compression. Technically the purpose of the data compression is to reduce duplicity in the stored and communicated data, thus increasing effective data density, Data compression is an important application in file storage and the distributed system. So if we need to increase the performance or heighten the speed so data compression is the advantage at that moment and compressed data is always suitable to reduce the cost both for storage and communication, data compression fundamentally diminishes the size of the data in terms of bits or bytes and the reduced data need lesser disk space compare to the data without compression. We have abundant ways to compress the data like text, audio, video files .Fundamentally we have two distinct characteristics for data compression and they are lossless and lossy compression. In lossless the integrity of the data is always maintained on the other hand In lossy compression data reduce by permanently eliminating certain amount of information, especially duplicate information, when a file in uncompressed, only a part of the original information is still there, Lossy compression commonly used for the audio and the video where certain amount of information is lost which is not examined by most of the users.