Clemson CPSC 863 - Multimedia Systems and Applications (8 pages)

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Multimedia Systems and Applications



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Multimedia Systems and Applications

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Lecture Notes


Pages:
8
School:
Clemson University
Course:
Cpsc 863 - Multimedia Sys/apps
Multimedia Sys/apps Documents

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An overview of Compression Compression becomes necessary in multimedia because it requires large amounts of storage space and bandwidth Types of Compression CpSc 863 Multimedia Systems and Applications Lossless compression data is not altered or lost in the process Lossy some info is lost but can be reasonably reproduced using the data Data Compression James Wang 2 Binary Image Compression Binary Image Compression RLE Run Length Encoding Disadvantage of RLE scheme Also called Packed Bits encoding E g aaaaaaaaaaaaaaaaaaaa111110000000 Can be coded as Byte1 Byte 2 Byte3 Byte4 Byte5 Byte6 20 a 05 1 07 0 This is a one dimensional scheme Some schemes will also use a flag to separate the data bytes When groups of adjacent pixels change rapidly the run length will be shorter It could take more bits for the code to represent the run length that the uncompressed data negative compression It is a generalization of zero suppression which assumes that just one symbol appears particularly often in sequences http astronomy swin edu au pbourke dataformats rle http datacompression info RLE shtml 3 http www data compression info Algorithms RLE 4 Basics of Information Theory According to Shannon the entropy of an information source S is defined as H S Lossless Compression Algorithms Entropy Encoding p log i i 2 1 pi where pi is the probability that symbol Si in S will occur log 2 Adapted from http www cs cf ac uk Dave Multimedia node207 html 1 pi indicates the amount of information contained in Si i e the number of bits needed to code Si For example in an image with uniform distribution of grey level intensity i e pi 1 256 then the number of bits needed to code each grey level is 8 bits The entropy of this image is 8 5 6 1 The Shannon Fano Algorithm The Shannon Fano Algorithm Encoding for the Shannon Fano Algorithm A simple example will be used to illustrate the algorithm Symbol A B C D E Count 15 7 6 6 5 7 A top down approach 1 Sort symbols according to their frequencies



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