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2015
Data compression is a requirement these days as it makes the communication of data faster. There are many techniques of data compression. The paper proposes a lossless method of data compression which is very simple and is very fast and efficient. It is a fixed length encoding of data and is very fast to decompress in comparison with the existing lossless compression techniques.
Data Compression is the technique through which, we can reduce the quantity of data, used to represent content without excessively reducing the quality of the content. This paper examines the performance of a set of lossless data compression algorithm, on different form of text data. A set of selected algorithms are implemented to evaluate the performance in compressing text data. A set of defined text file are used as test bed. The performance of different algorithms are measured on the basis of different parameter and tabulated in this article. The article is concluded by a comparison of these algorithms from different aspects.
Computing Research Repository, 2010
For storing a word or the whole text segment, we need a huge storage space. Typically a character requires 1 Byte for storing it in memory. Compression of the memory is very important for data management. In case of memory requirement compression for text data, lossless memory compression is needed. We are suggesting a lossless memory requirement compression method for text data compression. The proposed compression method will compress the text segment or the text file based on two level approaches firstly reduction and secondly compression. Reduction will be done using a word lookup table not using traditional indexing system, then compression will be done using currently available compression methods. The word lookup table will be a part of the operating system and the reduction will be done by the operating system. According to this method each word will be replaced by an address value. This method can quite effectively reduce the size of persistent memory required for text data. At the end of the first level compression with the use of word lookup table, a binary file containing the addresses will be generated. Since the proposed method does not use any compression algorithm in the first level so this file can be compressed using the popular compression algorithms and finally will provide a great deal of data compression on purely English text data.
International Journal of Engineering Research and, 2015
Data compression is now almost a common requirement for every applications as it is a means for saving the channel bandwidth and storage space. Data Compression is an art of allowing a technique to reduce the volume of data i.e. excess information, by maintaining the quality of data. There a number of algorithms available for compression of files of different formats. But, algorithm is to be such a chosen which reduces redundancy of data by consuming less time and providing more compression ratio as compared to other techniques. So, even for a single data type, numbers of approaches are available and to select among them the best one depending upon the need is very important and a difficult task. Compression methods are categorized as Lossy and Lossless but in this paper focus is only on Lossless text compression techniques. The methods which are discussed are Run Length Encoding, Shannon Fanon, Huffman, Arithmetic, LZ77, LZ78 and LZW with its performance.
International Journal of Computer Applications, 2012
Data compression is an effective means for saving storage space and channel bandwidth. There are two main types of compression lossy and lossless. This paper will deal with lossless compression techniques named Huffman, Arithmetic, LZ-78 and Golomb coding. The paper attempts to do comparative analysis in terms of their compression efficiency and speed. The test files used for this include English text files, Log files, Sorted word list and geometrically distributed data text file. The implementation results of these compression algorithms suggest the efficient algorithm to be used for a certain type of file to be compressed taking into consideration both the compression ratio and speed of operation. In terms of compression ratios, Golomb is best suited for very low frequency Text files, arithmetic for moderate and high frequency. Implementation is done using MATLAB software.
2008
While the basic of World Wide Web communication data almost of data still be represented by Text such as data exchange in Web Services base on XML technology and storage data into Relational Databases. Unfortunately, these attractive of data come at the expense of performance to transfer data. A way to improve is Compression technique. In this paper we present new compression algorithm using Capitalization. The mechanism has 3 steps is following: Firstly, Remove White space. Secondary, Compressing data to UpperCamelCase capitalization style and Lastly, to Decompress compressed data. Our experiments have shown significant performance gains of our algorithm include reduce data size up to 22% and keep data integrity. In additionally, compressed data is easy to read and understand like naming convention in several programming language.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.
2008 4th International Conference on Next Generation Web Services Practices, 2008
While the basic of World Wide Web communication data almost of data still be represented by Text such as data exchange in Web Services base on XML technology and storage data into Relational Databases. Unfortunately, these attractive of data come at the expense of performance to transfer data. A way to improve is Compression technique. In this paper we present new compression algorithm using Capitalization. The mechanism has 3 steps is following: Firstly, Remove White space. Secondary, Compressing data to UpperCamelCase capitalization style and Lastly, to Decompress compressed data. Our experiments have shown significant performance gains of our algorithm include reduce data size up to 22% and keep data integrity. In additionally, compressed data is easy to read and understand like naming convention in several programming language.
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.
International Journal of Computer Applications, 2017
In this current age both communication and generic file compression technologies are using different kind of efficient data compression methods massively. This paper surveys a variety of data compression methods. The aim of data compression is to reduce redundancy in stored or communicated data. Data compression has important application in the area of file storage and distributed system. This paper will provide an overview of several compression methods and will formulate new algorithms that may improve compression ratio and abate error in the reconstructed data. In this work the data compression techniques: Huffman, Run-Length, LZW, Shannon-Fano, Repeated-Huffman, Run-Length-Huffman, and Huffman-Run-Length are tested against different types of multimedia formats such as images and text, which shows the difference of various data compression methods on image and text file.
2013
1 Abstract: The size of data related to a wide range of applications is growing rapidly. Typically a character requires 1 Byte for storing. Compression of the data is very important for data management. Data compression is the process by which the physical size of data is reduced to save on memory or improve traffic speeds on a website. The purpose of data compression is to make a file smaller by minimizing the amount of data present. When a file is compressed, it can be reduced to as little as 25% of its original size which makes it easier to send to others over the internet. Data compression may take extensions such as zip, rar, ace, and BZ2. It is normally done using special compression software. Compression serves both to save storage space and to save transmission time. The aim of compression is to produce a new file, as short as possible, containing the compressed version of the same text. Grand challenges such as the human generated project involve very large distributed data...
2010
In this paper we use ternary representation of numbers for compressing text data. We use a binary map for ternary digits and introduce a way to use the binary 11-pair, which has never been use for coding data before, and we futher use 4-Digits ternary representation of alphabet with lowercase and uppercase with some extra symbols that are most commonly used in day to day life. We find a way to minimize the length of the bits string, which is only possible in ternary representation thus drastically reducing the length of the code. We also find some connection between this technique of coding dat and Fibonacci numbers.
International Journal of Engineering and Technology, 2017
In computer science, data compression or bit-rate reduction is a way to compress data so that it requires a smaller storage space making it more efficient in storing or shortening the data exchange time. Data compression is divided into 2 parts, Lossless Data Compression and Lossy Data Compression. Examples of Lossless methods are: Run Length, Huffman, Delta and LZW. While the example of Lossy method is: CS & Q (Coarser Sampling and / or Quantization). This paper analyze the Lossless method using Run Length Encoding (RLE) Algorithm, Arithmetic Encoding, Punctured Elias Code and Goldbach Code. This Paper also draw a comparison between the four algorithms in order to determine which algorithm is more efficient in doing data compression.
Journal of Techniques
Since the demand for data transfer and storage is always increasing, sending data in its original form will take a long time to send and receive. Compression is an important issue for digital communications systems because it imposes an important rule while reducing complexity and power requirements. The goal of compression is to reduce the file size without compromising the quality of the information, which leads to more capacity saving and reduces the required bandwidth in terms of the communications system. This paper proposes a system that consists of a hybrid of two lossless techniques, including a concatenation of Huffman and LZ4 in order to enhance the traditional techniques. The result of the proposed system demonstrates that the proposed combination techniques reduce the file size significantly, achieving between 73.649 % and 79.708 % in terms of average saving ratio (SR). The above would give us credible, cost-effective, and affordable lossless encoding systems for electro...
2014
Compression is useful because it helps us to reduce the resources usage, such as data storage space or transmission capacity. Data Compression is the technique of representing information in a compacted form. The actual aim of data compression is to be reduced redundancy in stored or communicated data, as well as increasing effectively data density. The data compression has important tool for the areas of file storage and distributed systems. To desirable Storage space on disks is expensively so a file which occupies less disk space is “cheapest” than an uncompressed files. The main purpose of data compression is asymptotically optimum data storage for all resources. The field data compression algorithm can be divided into different ways: lossless data compression and optimum lossy data compression as well as storage areas. Basically there are so many Compression methods available, which have a long list. In this paper, reviews of different basic lossless data and lossy compression ...
International Journal of Computer Applications, 2014
Lossless text data compression is an important field as it significantly reduces storage requirement and communication cost. In this work, the focus is directed mainly to different file compression coding techniques and comparisons between them. Some memory efficient encoding schemes are analyzed and implemented in this work. They are: Shannon Fano Coding, Huffman Coding, Repeated Huffman Coding and Run-Length coding. A new algorithm "Modified Run-Length Coding" is also proposed and compared with the other algorithms. These analyses show how these coding techniques work, how much compression is possible for these coding techniques, the amount of memory needed for each technique, comparison between these techniques to find out which technique is better in what conditions. It is observed from the experiments that the repeated Huffman Coding shows higher compression ratio. Besides, the proposed Modified run length coding shows a higher performance than the conventional one.
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.
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.
Data Compression is the science and art of representing information in a compact form. Compression is the process of coding that will effectively reduce the total number of bits needed to represent certain information.Data compression has been one of the critical enabling technologies for the ongoing digital multimedia revolution .data compression also called as source coding or bitrate reduction. There are different compression algorithms which are available in different formats. Data compressions are generally lossless and lossy data compression. In this paper, we study different methods of lossless data compression algorithms and calculating the entropy on English text files: Shanon-Fano coding, Huffman Encoding, RunLength Encoding (RLE), Lempel-Ziv-Welch (LZW). Keywords: lossless data compression, lossy data compression, Entropy, Shannon-Fano coding, Huffman encoding, RLE, LZW.
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