Lossy vs. Lossless Data Compression - Assignment Example

Lossy vs. Lossless Data Compression 1- Introduction Sayood (2000) outlines that data compression is most valuable in communications for the reason that it facilitates devices to store or transmit the similar amount of data but using small size and fewer bits. However, there are a range of techniques to implement the process of data compression, but simply a few of them have been standardized. The Comité Consultatif International Téléphonique et Télégraphique (CCITT) is an association that has specified the particular rules for the standard data compression. There are different methods intended for transmitting faxes as well as a compression standard designed for data communications by making use of modems. In addition, there are various file compression formats, like that ZIP and ARC (Sayood, 2000). However, according to Lelewer & Hirschberg (2009), the data compression is frequently acknowledged as coding. Here, the term coding is extensively identified as general term that entitles some particular representation of data that fulfils a given requirement. In addition, the data compression can be taken as a main branch of information theory that is intended to trim down the quantity of data to be sent (Lelewer & Hirschberg, 2009). This report will provide a detailed analysis of the paradigm of data compression. The aim of this research is to investigate the areas and theories behind the data compression, its implementation and potential benefits. 2- Need for data compression Blelloch (2001) outliend that a straightforward description of data compression is that “it entails transforming a string of characters into a number of demonstrations (like that ASCII) into a novel type of string (that can be string of bits) that holds the similar information however whose length is as small as probable”. However, the data compression is significantly used or implemented in the areas of data storage and data transmission. Since, a lot of data management and processing applications necessitate storage of huge volumes of data, as well as the amount of similar applications is continually augmenting like the utilization of computers enlarges to novel disciplines. At the similar time, the propagation of computer data and information communication networks is consequential in huge transition of data over communication network links (Blelloch, 2001; Lelewer & Hirschberg, 1987). Thus, the data compressing the intended data to be transmitted or stored on reduced storage that minimizes the overall communication costs. In this way the storage or transmission capacity of data reduced, which augment and enhance the capability of the communication channel. In addition, compressing a file to its half size as compared to original size offers advantages of transmitting more data or store double capacity data. Thus, it can turn out to be practicable to store the huge amount of data at a higher capacity, as well as at quick speed. Furthermore, it also manages the intensity of the storage hierarchy as well as minimizes the load on the output or input channels of the communication network or computer system (Data-Compression, 2010). 3- Lossy vs. Lossless data compression Harris (2009) outlined that lossless compression and lossy compression are two main categories of the data compression. In case of lossless compression we work with original file exactly to recreate it. In addition, the lossless data compression is based on the design of breaking a data file into little structure for storage or transmission as well as then setting it back jointly on the receiving end therefore it could be utilized again. On the other hand, lossy compression operates in extremely different way. Since, these applications basically eradicate "needless" bits of data or information, tailoring the data or information file consequently that it is smaller. Thus, this kind of data or information compression is utilized for minimizing the file size especially for bitmap pictures that are inclined to be quite bulky. However, a lossless compression application cannot perform a great deal through this type of file, as huge parts of the picture could be similar; thus, the majority of the individual pixels are a little bit dissimilar (Radified, 2009; Harris, 2009). However, to create a smaller size picture without loosing its resolution, we need to transform the color value intended for definite pixels. In addition, by using lossy data compression, we cannot obtain the real data file back following it has been compressed. Through this technique we are stuck by the data compression program's reinterpretation of the original data file (Bobulous, 2009). In this case we cannot make use of this sort of data compression technique for anything that requires reproduction accurately, encompassing databases, software applications or presidential induction speeches (Harris, 2009) & (Tobin, 2009). 4- Text compression methods This section will discuss about the different text compression methods. According to Yoshida, Morihara, Yahagi, & Satoh (1999), the text compression and pattern matching algorithms are two significant subjects in the extensive field of text processing. These text compression and pattern matching algorithms are implemented to deal with the texts (word editors), to the management and handling the storage of text compression (textual data), as well as to data repossession systems (full text search). In addition, they are fundamental components employed in development of the practical software executing on the majority of operating systems. Furthermore, they highlight programming techniques that provide standards in other areas of computer science (software or system design). Also, they play a significant role in theoretical computer science by offering solutions to challenging problems (Yoshida et al., 1999; Crochemore & Lecroq, 1996). 4.1- Run length encoding Crochemore & Lecroq (1996) discuss about one of the main text compression algorithms, run length encoding (RLE). However, the Run-length encoding (RLE) is an extremely straightforward structure of data compression through which desired data are stored like a single data value as well as count, relatively than as the unique run. In addition, the Run-length encoding is used in sequences through which the similar data value takes place in a lot of successive data elements. Thus, this is a very practical method on data that holds a lot of similar runs: for instance, comparatively simple graphic images like that line drawings, icons and animations. Furthermore, it is not practical by files that do not have a lot of runs because it could probably double the file size (Crochemore & Lecroq, 1996). 4.2- Huffman Encoding Bol'shakov & Smirnov (2005) discuss about the Huffman coding as a text compression algorithm. According to them, in information theory and computer science, Huffman text compression algorithm coding is an entropy encoding technique employed for lossless data compression. In addition the Huffman text compression algorithm make use of a variable-length code table intended for encoding a source symbol like that a character in a file. Since, through utilization of this method the variable-length code table has been resultant in some method foundational upon the estimated probability of happening for every potential value of the source symbol. Furthermore, David A. Huffman developed Huffman text compression algorithm. This algorithm was published in the 1952 (Bol'shakov & Smirnov, 1991). 5- JPEG image compression The file compression is valuable however compressing and reducing the size of the images is almost a basic requirement for the web development. Since, a common image occupies a great deal additional storage as compared to a usual text message, also without compression images would be harder to distribute and store (Vectorsite, 2009). Here Joint Photographic Experts Group (JPEG) compression permits us to select whether we choose a JPEG image file to make a smaller file of inferior quality or huge as well as of high quality image. In addition, the JPEG images compression provides us image with less capacity and simpler quality regarding the web based data storage as well as publishing.  However, in case of the publishing images on the internet, a number of middle courses are suggested for the reason that big files will acquire a great deal time to come into view on the viewer’s screen. Furthermore, the JPEG image compression does not transform the height and width of an image but transforms only the size of file (Digital-Photo-Software-Guide, 2009). Lane (2009) state that JPEG images are uncompressed and having usually 24 bits/pixel. However, the most excellent recognized lossless compression techniques can reduce similar data to approximately 2:1 on standard. JPEG is able to usually attain 10:1 to 20:1 image compression with no visible loss, carrying the efficient storage constraint down to 1 to 2 bits for each pixel. 50:1 or 30:1 image compression is probable through small to reasonable defects, as for very-low-quality reasons like that archive or previews indexes, 100:1 image compression is quite possible. An image condensed 100:1 by using JPEG takes up the similar space like a complete color one tenth level thumbnail image, however it keeps a great deal additional detail than as a thumbnail (Lane, 2009). Wallace (1991) outlined that the development in the area of JPEG and continuous tone data compression standard are not a universal remedy that will resolve the numerous problems those have to be tackled previous to digital images will be completely incorporated inside the entire the applications that will eventually be profitable to them. For instance, if two applications are not able to exchange uncompressed images for the reason that they utilize mismatched aspect ratios, color spaces, dimensions, and so on. Consequently, here an ordinary compression technique will not facilitate. (Wallace, 1991). 6- Conclusion This report has offered a detailed analysis of the data compression. Data compression has turned out to be a main aspect of the web based data transfer, communication and software application development. This paradigm of the information technology has facilitated greatly regarding the easy and faster data transfer. I hope this research will offer an insight into the overall analysis of the data compression. 7- References Blelloch, G.E., 2001. Introduction to Data Compression. [Online] Available at: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/pscico-guyb/realworld/www/compression.pdf [Accessed 15 February 2010]. Bobulous, 2009. Lossless and lossy audio formats for music. [Online] Available at: http://www.bobulous.org.uk/misc/audioFormats.html [Accessed 16 February 2010]. Bol'shakov, I.A. & Smirnov, A.V., 1991. Text compression methods. Journal of Mathematical Sciences, 56(1), pp.2249-2262. Crochemore, M. & Lecroq, T., 1996. Pattern matching and text compression algorithms. [Online] Available at: http://www-igm.univ-mlv.fr/~mac/REC/DOC/B5-survey.html#tc [Accessed 16 February 2010]. Crochemore, M. & Lecroq, T., 1996. Pattern-matching and text-compression algorithms. ACM Computing Surveys (CSUR) , 28(01), pp.39-41. Data-Compression, 2010. Theory of Data Compression. [Online] Available at: http://www.data-compression.com/theory.shtml [Accessed 16 February 2010]. Digital-Photo-Software-Guide, 2009. JPEG Compress. [Online] Available at: HYPERLINK "http://www.digital-photo-software-guide.com/jpeg-compress.html" http://www.digital-photo-software-guide.com/jpeg-compress.html [Accessed 15 February 2010]. Harris, T., 2009. How File Compression Works. [Online] Available at: http://computer.howstuffworks.com/file-compression3.htm [Accessed 16 February 2010]. Lane, T., 2009. JPEG image compression FAQ, part 1/2. [Online] Available at: http://www.faqs.org/faqs/jpeg-faq/part1/ [Accessed 16 February 2010]. Lelewer, D.A. & Hirschberg, D.S., 1987. Data Compression. Computing Surveys (CSUR), 19(03), pp.261-296. Lelewer, D.A. & Hirschberg, D.S., 2009. Data Compression. [Online] Available athttp://www.ics.uci.edu/~dan/pubs/DataCompression.html [Accessed 16 February 2010]. Radified, 2009. Monkey's Audio Lossless Compressor. [Online] Available at: http://mp3.radified.com/lossless.htm [Accessed 16 February 2010]. Sayood, K., 2000. Introduction to Data Compression. 2nd ed. New York: Morgan Kaufmann. Tobin, M., 2009. Effects of Lossless and Lossy Image Compression and Decompression on Archival Image Quality in a Bone Radiograph and an Abdominal CT Scan. [Online] Available at http://www.mikety.net/Articles/ImageComp/ImageComp.html [Accessed 16 February 2010]. Vectorsite, 2009. [2.0] JPEG Image Compression. [Online] Available at http://www.vectorsite.net/ttdcmp_2.html [Accessed 16 February 2010]. Wallace, G.K., 1991. The JPEG Still Picture Compression Standard. Communications of the ACM, 34(4), pp.30-44. Yoshida, S., Morihara, T., Yahagi, H. & Satoh, N., 1999. Application of a Word-Based Text Compression Method to Japanese and Chinese Texts. In Data Compression Conference, 1999. Proceedings. DCC '99. Snowbird, UT , 1999. IEEE Computer Society Washington, DC, USA. Read More