Data comparation

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Data comparation

  1. 1. DATA COMPRESSION Prepared by – JAYPAL SINGH CHOUDHARY SOURABH JAIN Graphics from - http://plus.maths.org/issue23/features/data/data.jpg
  2. 2. Why Data Compression  Definition: Reducing the amount of data required to represent a source of information. Preserve the output data original to the input as much as possible.  Objectives: Reduce the space required for the data storage. Also reduce the time of data transmission over network. SOURCES - www.data-compression.com/index.shtml
  3. 3. Types of Compression  Lossless compression.  Lossy compression. Basic principle of both : Graphics from - http://img.zdnet.com/techDirectory/LOSSY.GIF
  4. 4. Lossless Compression In this the compressing and decompressing algorithms are inverse of each other. TECHNIQUES :  Run-Length Encoding. When data contains repeated strings then these can be replaced by special marker. original data compressed data Sources- www.data-compression.com/lossless.shtml 572744444444321333333333335278222222 5727#408321#3115278#206
  5. 5. Lossless (contd.)  Statistical compression: In this the short codes are used for frequent symbols and long for infrequent. Three common principles are :- 1. Morse code. 2. Huffman encoding. 3. Lempel- Ziv -Welch encoding.  Relative compression: Extremely useful for sending video, commercial TVs and30 frames in every second. References - www.data-compression.com/lossless.shtml
  6. 6. Lossy compression  Some data in output is lost but not detected by users.  Mostly used for pictures, videos and sounds. Basic techniques are : 1. JPEG 2. MPEG Referenced -http://searchciomidmarket.techtarget.com/sDefinition/0,,sid183_gci214453,00.html Transformation Quantisation Encoding decompress compress
  7. 7. Latest Developments  Fathom 3.0 Developed by Inlet technologies in cooperation with Microsoft and Scientific Atlanta. Work with media files for mobiles, portable, web and high definition.
  8. 8. Histor􀁜 A literature compendium for a large variety of Audiocoding systems was published in the IEEE Journal on Selected Areas in Communications (JSAC), February 1988. While there were some papers from before that time, this Collection documented an entire variety of finished, working audio coders, nearly all of them using perceptual (i.e. masking) Techniquce and some kind of frequency analysis and back End noiseless coding.
  9. 9. Image Compression Using Neural Networks Overview : - Introduction to neural networks. Back Propagated (BP) neural network. - Image compression using BP neural network. - Comparison with existing image compression techniques
  10. 10. Image Compression using BP Neural Network - Future of Image Coding(analogous to Our visual system). - Narrow Channel K-L. transform . - The entropy coding of the state vector h i's at the hidden Layer.
  11. 11. Image Compression using continued… - A set of image samples is used to train the network. - This is equivalent to compressing the input into the narrow channel and then reconstructing the input from the hidden layer.
  12. 12. - The image to be subdivided into non-overlapping blocks of n x n pixels each. Such block represents N-dimensional vector x, N = n x n, in N-dimensional space. Transformation process maps this set of vectors into y=W (input) output=W-1y Transform coding with multilayer Neural Network:
  13. 13. Image Compression continued… The inverse transformation need to reconstruct original image with minimum ofdistortions.
  14. 14. Proposed Method: - Wavelet packet decomposition. - Quantization. - Organization of vectors. - Neural network approximation. - Lossless encoding and reduction
  15. 15. Wavelet Packet Decomposition The image is first put through a few levels ofwavelet packet decomposition.
  16. 16. Quantization - Each of the decomposed wavelet sections is divided by the quantization value and rounded to the nearest integer. - This creates redundancy in the data which is easier to work with. - Quantization is not lossless.
  17. 17. Neural Network Approximation -An example of the vector with the trained Neural network attempting to fit it.
  18. 18. Lossless Encoding and Reduction - The entire data stream is then run- lengthencoded (RLE). - Afterwards, we can save the data using the ZIP file format, which applies some other lossless encoding methods.
  19. 19. Conclusion - Neural networks can be used to compress images! - However, they are probably not the best way to go unless the data can be represented in some easier way. - Most of the compression came from the quantization, organization, and Lossless compression stages.
  20. 20. References 1. http://en.wikipedia.org/wiki/Data_comp ression 2. http://en.wikipedia.org/wiki/Lossless_d ata_compression 3. http://en.wikibooks.org/wiki/Data_Codi ng_Theory/Data_Compression 4. http://en.wikibooks.org/wiki/Data_Com pression 5. http://datacompression.dogma.net/inde x.php?title=Comp.compression_FAQ
  21. 21. Annotated Bibliography  I choose the text from – www.data-compression.com/index.shtml www.data-compression.com/lossless.shtml http://searchciomidmarket.techtarget.com/sDefinition/0,,sid183_gci214453 ,00.html http://localtechwire.com/business/local_tech_wire/wire/story/1276887 http://www.futureofgadgets.com/futureblogger/show/1730 because it fulfills mine requirement for the topic.  I choose the graphics from – http://img.zdnet.com/techDirectory/LOSSY.GIF http://plus.maths.org/issue23/features/data/data.jpg because it clears the situation which I want to explain.

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