Data Compression I. Introduction In the modern era known as the “Information Age,” forms of electronic information are steadily becoming more important. Unfortunately, maintenance of data requires valuable resources in storage and transmission, as even the presence of information in storage re-quires some power. However, some of the largest files are those that are in formats re-plete with repetition, and thus are larger than they need to be. The study of data compres-sion is the science which attempts to advance toward methods that can be applied to data in order to make it take up less space. The uses for this are vast, and algorithms will need to be improved in order to sustain the inevitably larger files of the future. Thus, I decided to focus my research on the techniques that successful methods use in order to save space. My research question: What common features do good methods of data compression share? II. Mathematical Context The history of data compression is not so much a continent of improvement as it is an archipelago of dispersed -but related- innovations in the subject of information theory. The reason for this is mostly its relatively new development. Many topics in mathematics are now mostly researched in terms of computing. However, most of these subjects were already fairly developed before the arrival of computers. Cryptography, for example, was used since ancient times to keep information secret, and has only now developed into methods that assume the use of a computer. In contrast, computers are almost a require-ment for data compression as a theory to be of practical utilization: Analog information can easily be compressed by recording it in less space. Anyhow, there is rarely a need to store info... ... middle of paper ... ...hm).” “LZW.” „Minimum description length.” “Run-length Encoding.” “Shannon-Fano coding.” “Wikipedia:Citing Wikipedia” "Historical Notes: History [of data compression]." [Excerpt from book; A New Kind of Science, by Stephen Wolfram] ©2002 Stephen Wolfram. Wolfram Science. 31 July 2005 . Lynch, Thomas D. Data Compression: Techniques and Applications. Belmont, California, Lifetime Learning Productions. ©1985 Solomon, David. Data Compression: The Complete Reference. New York, Springer Verlag. ©1998 Appendix B: Mathematica Code for BLM 8
Due to compression, TV audio and video require less bandwidth and multiple digital TV channels can fit unto one satellite transponder as oppose to a single analogue channel occupying the whole transmission line.
Perhaps the two most crucial elements of the success of such systems are that they allow an incredible number of files to be gathered through the amalgamation of the files on many computers, and that increasing the value of the databases by adding more files is a natural by-product of using the tools for one's own benefit[7].
Nowadays, people are living in the data world. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in 2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes. A zettabyte is 〖10〗^21 bytes, or equivalently one thousand exabytes, one million petabytes, or one billion terabytes. That’s roughly the same order of magnitude as one disk drive for every person in the world [1].
Machine learning techniques represent the main source of data mining algorithms. Most of machine learning methods require data to be resident in memory while executing the analysis algorithm. Due to the huge amounts of the generated streams, it is absolutely a very important concern to deign space efficient techniques that can have only one look or less over the incoming stream.
This white paper identifies some of the considerations and techniques which can significantly improve the performance of the systems handling large amounts of data.
There are a great number of applications for Digital Signal Processing and in order to better understand why DSP has such a large impact on multiple aspects of society, it helps to better understand the wide variety of applications it can be used for. Here we will briefly look into the following applications of Digital Signal Processing and their uses; speech and audio compression, communications, biomedical signal processing and applications in the automobile manufacturing industry. Li Tan [1] goes into detail with each of these applications in his book, Digital Signal Processing, and explains how each are used on a daily basis.
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been stated that the amount of data doubles approximately
image or a video sequence. A Compression algorithm takes an input X and generates compressed information that requires fewer bits. The Decompression algorithm reconstructs the compressed information and gives the original.
Smith, E. (1993, June). On the shoulders of giants: from Boole to Shannon to Taube The origins and development of computerized information from the mid-19th century to the present. Information Technology and Libraries, 12, 217-226.
As we all know that Exascale computers runs million processors which generates data at a rate of terabytes per second. It is impossible to store data generated at such a rate. Methods like dynamic reduction of data by summarization, subset selection, and more sophisticated dynamic pattern identification methods will be necessary to reduce the volume of data. And also the reduced volume needs to be stored at the same rate which it is generated in order to proceed without interruption. This requirement will present new challenges for the movement of data from one super computer to the local and remote storage systems. Data distribution have to be integrated into the data generation phase. This issue of large scale data movement will become more acute as very large datasets and subsets are shared by large scientific communities, this situation requires a large amount of data to be replicated or moved from production to the analysis machines which are sometimes in wide area. While network technology is greatly improved with the introduction of optical connectivity the transmission of large volumes of data will encounter transient failure and automatic recovery tools will be necessary. Another fundamental requirement is the automatic allocation, use and release of storage space. Replicated data cannot be left
and sectors. What other option do we have to store massive amount of data? We
An image as perceived in "reality" is thought to be a function of two real variables, for instance, a(x, y) with a certain level of brightness of the image at the real coordinates (x, y). Further, an image may be considered to contain sub-images now and mentioned to as areas of-investment or ROI’s, or basically regions. This idea reflects the way that images as often as possible contain build-ups of items each of which can be the idea for a region.
When World War II broke out in 1939 the United States was severely technologically disabled. There existed almost nothing in the way of mathematical innovations that had been integrated into military use. Therefore, the government placed great emphasis on the development of electronic technology that could be used in battle. Although it began as a simple computer that would aid the army in computing firing tables for artillery, what eventually was the result was the ENIAC (Electronic Numerical Integrator and Computer). Before the ENIAC it took over 20 hours for a skilled mathematician to complete a single computation for a firing situation. When the ENIAC was completed and unveiled to the public on Valentine’s Day in 1946 it could complete such a complex problem in 30 seconds. The ENIAC was used quite often by the military but never contributed any spectacular or necessary data. The main significance of the ENIAC was that it was an incredible achievement in the field of computer science and can be considered the first digital and per...
Image compression is the art and science of reducing the amount of data required to represent an image. The purpose for image compression is to reduce the amount of data required for representing sampled digital images and therefore reduce the cost for storage and transmission. Image compression plays a key role in many important applications, including image database, image communications, remote sensing.
The fist computer, known as the abacus, was made of wood and parallel wires on which beads were strung. Arithmetic operations were performed when the beads were moved along the wire according to “programming” rules that had to be memorized by the user (Soma, 14). The second earliest computer, invented by Blaise Pascal in 1694, was a “digital calculating machine.” Pascal designed this first known digital computer to help his father, who was a tax collector. Pascal’s computer could only add numbers, and they had to be entered by turning dials (Soma, 32). It required a manual process like its ancestor, the abacus. Automation was introduced in the early 1800’s by a mathematics professor named Charles Babbage. He created an automatic calculation machine that was steam powered and stored up to 1000 50-digit numbers. Unlike its two earliest ancestors, Babbage’s invention was able to perform various operations. It relied on cards with holes punched in them, which are called “punch cards.” These cards carried out the programming and storing operations for the machine. Unluckily, Babbage’s creation flopped due to the lack of mechanical precision and the lack of demand for the product (Soma, 46). The machine could not operate efficiently because technology was t adequate to make the machine operate efficiently Computer interest dwindled for many years, and it wasn’t until the mid-1800’s that people became interested in them once again.