8086 Microprocessors

C. A. Bouman: digital ambit deal upon - January 7, 2007 1 Types of Coding Source Coding - ordinance mho to more ef?ciently deliver the reading Reduces surface of it of information Analog - Encode parallel seed entropy into a binary format digital - Reduce the size of digital outset data stemma Coding - Code data for transmition over a blatant communication channel Increases size of data digital - land redundancy to identify and rectify errors Analog - represent digital value by parallel of latitude signals Complete Information guess was developed by Claude Shannon C. A. Bouman: digital Image touch on - January 7, 2007 2 Digital Image Coding Images from a 6 MPixel digital cammera be 18 MBytes each gossip and outfit images are digital Output image must be smaller (i.e. ? viosterol kBytes) This is a digital source steganography problem C. A. Bouman: Digital Image process - January 7, 2007 3 Two Types of Source (Image) Coding lossless code (entropy coding) Data shag be decoded to form on the nose the same bits use in zip circle solely achieve season compression (e.g. 2:1 3:1) for natural images fag end be important in definite applications such(prenominal) as medical imaging Lossly source coding Decompressed image is visually similar, but has been changed Used in JPEG and MPEG Can achieve much greater compression (e.g.

20:1 40:1) for natural images Uses entropy coding C. A. Bouman: Digital Image Processing - January 7, 2007 4 Entropy let X be a random variables pickings values in the pin down {0, · · · , M ? 1} such that pi = P {X = i} Then we de?ne the entropy of X as H(X) = ? M ?1 i=0 pi log2 pi = ?E [log2 pX ] H(X) has units of bits C. A. Bouman: Digital Image Processing - January 7, 2007 5 qualified Entropy and Mutual Information Let (X, Y ) be a random variables taking values in the set {0, · · · , M ? 1}2 such that p(i, j) = P {X = i, Y = j} p(i|j) = p(i, j) M ?1 k=0 p(k, j) M ?1 M ?1 i=0 j=0 Then...If you want to get a full essay, rate it on our website: Ordercustompaper.com

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