ΔRLE

Data compression is widely required in the era of Information-communication-Technology (ICT), where it can be used to conserve the energy of networks, because a file with reduced size requires less time to get passed over the network. Thus the technique of compression and decompression can be quite effective in establishing efficient communication over the computer networks. The work performed in the paper, compares the Loss less data compression algorithms and analyses various parameters like compression ratio, compression speed, decompression speed, saving percentage. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.

This paper presents a linear predictor (LP)-based lossless sensor data compression algorithm for efficient transmission, storage and retrieval of seismic data. Auto-Regressive with eXogenous input (ARX) model is selected as the model structure of LP. Since earthquake ground motion is typically measured at the base of monitored structures, the ARX model parameters are calculated in a system identification framework using sensor network data and measured input signals. In this way, sensor data compression takes advantage of structural system information to maximize the sensor data compression performance. Numerical simulation results show that several factors including LP order, measurement noise, input and limited sensor number affect the performance of the proposed lossless sensor data compression algorithm concerned. Generally, the lossless data compression algorithm is capable of reducing the size of raw sensor data while causing no information loss in the sensor data. Copyright © 2005 John Wiley & Sons, Ltd.

Analysis for loss less data compression delivers the relevant data about variations of them as well as to describe the possible causes for each algorithm and best performing data types. It describes the basic lossless techniques of data compression Huffman encodes, Arithmetic Encoding, and Lempel Ziv Encodings then briefly with their effectiveness under varying data types of Latin text, audio and video. These properties give the solution of which lossless compression algorithm more suitable compared to other from the Saving Percentage, compression ratio, time of compression and time of decompression with Low Bandwidth Network. Moreover here Lossless Data Compression Algorithms (LDCA) being implemented and tested Huffman compression, Arithmetic compression, and Lempel Ziv algorithms, the implemented result shows that LZW algorithm saves more size than that of the others two with text file, Huffman compression algorithm saves more file sizes and the time takes to compressed decompress is higher than that of other two for audio file type and finally Huffman performs greater on very huge data compressions that is due to much compressing capability.

A prototype chip, called RD53A, has been designed by the RD53 collaboration to face the very high hit and trigger rate requirements (up to 3 GHz/cm2 and 1 MHz, respectively) of the High Luminosity LHC experiment upgrades. In this paper, an improved algorithm for data compression, capable of sustaining the very high data volume and proposed to be implemented in the periphery of the chip, is presented: it exploits Run Length Encoding (RLE) and Variable Length Coding (VLC) to compact chip pixel hit patterns. The compression and decompression algorithms are implemented with MATLAB, and the performance is calculated taking into account the RD53A data readout implementation and its chip simulation and verification framework (called VEPIX53). In all considered cases, the results show that the RLE and VLC combination achieves a data compression ratio between 1.57 and 1.62, resulting in a bitstream size reduction between 36.2% and 38.4% with respect to the rate of the current data transmission format.

Efficient utilization of energy is a core area of research in wireless sensor networks. Data compression methods to reduce the number of bits to be transmitted by the communication module will significantly reduce the energy requirement and increase the lifetime of the sensor node. Through analysis the probability distribution of sensor data and a classic compression algorithm named ND-Encoding, this paper proposed a new algorithm specifically designed for lossless data compression in sensor nodes called Difference Fitting Residuals compression algorithm. The proposed algorithm will calculate the linear fitting values of sensor data's differences and then calculate the fitting residuals which will be input to an entropy encoder to achieve data compression. Compared with two typical lossless compression algorithms, the proposed algorithm indicated better compression ratios, despite a less computational effort.

Today, there is a huge demand for data compression due to the need to reduce the transmission time and increase the capacity of data storage. Data compression is a technique which represents an information, images, video files in a compressed or in a compact format. There are various data compression techniques which keep information as accurately as possible with the fewest number of bits and send it through communication channel. Arithmetic algorithm, Lempel–Ziv 77 (LZ77) and run length encoding with a K-precision (K-RLE) algorithms are lossless data compression algorithms which have lower performance rate because of their processing complexity as well as execution time. This paper presents an efficient parallel approach to reduce execution time for compression algorithms. The proposed OpenMP is an efficient tool for programming within parallel shared-memory environments. Finally, it shows that performance parallel model experimented using Open Multi-Processing (OpenMP) Application Programming Interface through Intel Parallel studio on multicore architecture platform with spec of Core 2 duo—2.4 GHz, 1 Gb RAM machine of parallel approach for compression algorithms has been improved remarkably against sequential approach. The improvement in compression ratio through an efficient parallel approach leads to reduction on transmission cost, reduction in storage space and bandwidth without additional hardware infrastructure. An overall performance evaluation shows arithmetic data compression algorithm with 46% which is better than LZ77 of 44% as well as K-RLE of 37% data compression algorithms.

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 algo

Summary form only given. The World Wide Web Consortium (W3C) standard synchronized multimedia integration language (SMIL) is an HTML-like mark-up language to describe temporal behavior and presentation layout for multimedia objects. SMIL is widely used in today's Internet. Many video clips send SMIL documents to clients before transmitting video and audio streams. Data compression is a process to reduce the number of bits in a representation of data. It can save storage space and reduce bandwidth requirement. Data compression technologies may be classified into two categories: lossless and lossy. The compression technology that can recover data perfectly from the compressed bits is called lossless compression. The one that cannot is called lossy compression. There are many lossless data compression algorithms, such as Ziv-Lempel methods, that have been developed in recent years. However, SMIL document compression performance with such methods is not very impressive because they do not exploit the special format of the SMIL document. As a result, the rate to compress a SMIL document is almost the same as that to compress English text, i.e., 2-4 bits/char. For better compression, a method based on a new parsing technology is proposed. The proposed compression algorithm contains two major procedures: parsing and coding. Parsing is a procedure to segment a SMIL document into non-overlapping phrases (a string of consecutive characters). Coding is a procedure to assign a codeword to the phrase. Both the phrase and codeword are of variable length.

ObjectiveData compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single-channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals is proposed, maintaining all dominant features. MethodsThe single-channel data are first quantized using optimal quantization level, so that fewer number of bits are needed to represent it, maintaining low quantization error. Then, second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. ResultsThis algorithm was tested on various single-lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single-channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. ConclusionThis algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. SignificanceA comparative study of the proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

The article describes methods used for data compression without considering formats or types. Data compression is the process of encoding data to reduce its size; lossless data compression means that the reverse decoding process restores the data in its original form. There are limitations to lossless data compression that depend on the information entropy of the message: the lower it is, the greater the potential compression ratio of this data. Data with high entropy, for example, random or previously compressed with a sufficiently optimal encoding, cannot be compressed. The work aims to investigate using data compression algorithms with different types of information, formats or information entropy. Lossless data compression algorithms are divided into subcategories, in particular, dictionary and entropy, which differ in the principle of operation. Dictionary and entropy methods can also be combined to increase compression efficiency. The scientific novelty lies in finding patterns between the algorithms used and the data compression ratios of specific formats. For the first time, data on many different data compression algorithms, both independent and those consisting of others, were processed and systematized. As a result, data were obtained on compression methods best suited for compressing images, Internet pages, source code, and other widely used formats. The research methodology is based on measuring several characteristics of the original and compressed files and the operation of algorithms with subsequent comparison of this data. Therefore, files can be distinguished by format and information entropy before compression. After compression, a compression ratio can be found that characterizes the efficiency of the algorithms. The study involves universal algorithms that perceive information as a specific sequence of bytes. Thus, they can be applied to various file formats, including those most often used in distributed data storage systems. The conclusion contains practical recommendations for the application of data compression algorithms. The data obtained during the study can be used for integration into other software products or further analysis.

Data Compression may be defined as the science and art of the representation of information in a crisply condensed form. For decades, Data compression has been one of the critical enabling technologies for the ongoing digital multimedia revolution. There are a lot of data compression algorithms which are available to compress files of different formats. This paper provides a survey of different basic lossless data compression algorithms. Experimental results and comparisons of the lossless compression algorithms using Statistical compression techniques and Dictionary based compression techniques were performed on text data. Among the Statistical coding techniques, the algorithms such as Shannon-Fano Coding, Huffman coding, Adaptive Huffman coding, Run Length Encoding and Arithmetic coding are considered. Lempel Ziv scheme which is a dictionary based technique is divided into two families: one derived from LZ77 (LZ77, LZSS, LZH, LZB and LZR) and the other derived from LZ78 (LZ78, LZW, LZFG, LZC and LZT). A set of interesting conclusions are derived on this basis.

As data is being produced in an unprecedented rate, lossless data compression has become an important step in data storage and transmission processing as it helps to reduce the resource usage in these fields. However, the current bottlenecks of existing lossless data compression tools causes the compression and decompression process to be very time consuming for large-scale data processing. General purpose computing on graphic processing units (GPUs) introduces new opportunities where parallelism is available and this could be the solution to address the bottlenecks of the data compression. Several parallel lossless data compression algorithms on GPU have been proposed but there isn't much comparative study conducted on the performance among them. This paper examines the existing CUDA lossless data compression algorithms and compares their performance. These CUDA data compression algorithms are evaluated and tested on different datasets of different sizes. The article is concluded by a comparison of these CUDA lossless data compression algorithms from different aspects.

In this paper, we present parallel algorithms for lossless data compression based on the Burrows-Wheeler Transform (BWT) block-sorting technique. We investigate the performance of using data parallelism and task parallelism for both multi-threaded and message-passing programming. The output produced by the parallel algorithms is fully compatible with their sequential counterparts. To balance the workload among processors we develop a task scheduling strategy. An extensive set of experiments is performed with a shared memory NUMA system using up to 120 processors and on a distributed memory cluster using up to 100 processors. Our experimental results show that significant speedup can be achieved with both data parallel and task parallel methodologies. These algorithms will greatly reduce the amount of time it takes to compress large amounts of data while the compressed data remains in a form that users without access to multiple processor systems can still use.

Data compression in environmental monitoring systems is indispensable, to save the power of the monitoring nodes and reduce the size of system database as well as increasing the efficiency of data throughput. In this article, a new lossless compression algorithm has been proposed based on combining of three famous compression methods which are: Delta encoding, Run-length encoding and Huffman encoding. The main advantage of the proposed algorithm is its simplicity, which makes it suitable for systems with very limited resources. The proposed algorithm has been applied to measured samples extracted from a research monitoring system. The results indicated that the compression ratio of the initial data by the proposed algorithm reaches 90%, which leads to a significant preservation of the storage space for the system.

The amount of data that is stored and transferred grows regularly and rapidly. When it comes to transferring large data volumes, a data compression algorithm can be useful. A well-chosen data compression algorithm can reduce the size of the data to up to 60%. The problem of creating new and modifying or optimizing old algorithms is up to date. This article discloses some of the most widespread algorithms of data comparison; more exactly, four of them. Shannon–Fano coding, named after Claude Shannon and Robert Fano, is a technique for constructing a prefix code based on a set of symbols and their probabilities. It is suboptimal in the sense that it does not achieve the lowest possible expected code word length like Huffman coding. Huffman code is a particular type of the optimal prefix code that is commonly used for lossless data compression. The process of finding or using such a code proceeds by means of Huffman coding; an algorithm developed by David A. Huffman. Lempel–Ziv–Storer–Szymanski (LZSS) is a lossless data compression algorithm, a derivative of LZ77 that was created in 1982 by James Storer and Thomas Szymanski. The main difference between LZ77 and LZSS is that in LZ77 the dictionary reference could actually be longer than the string it was replacing. In LZSS, such references are omitted if the length is less than the “break even” point. Furthermore, LZSS uses one-bit flags to indicate whether the next chunk of data is a literal (byte) or a reference to an offset/length pair. Lempel–Ziv–Welch (LZW) is a lossless data compression algorithm created by Abraham Lempel, Jacob Ziv, and Terry Welch. It was published as an improved implementation of the LZ78 algorithm. As part of the work on the article, these algorithms were implemented and an experimental analysis of their quality and speed was carried out. Those experiments gave the conclusion that the best compression speed results were shown by LZSS and the best compression ratio was reached by LZW. The work can be useful for researchers in the field of data compression.