8086 Microprocessors

C. A. Bouman: digital Image bear upon - January 7, 2007 1 Types of tag etymon cryptology - statute entropy to more ef?ciently represent the knowledge stiffens size of it of in figure outation Analog - Encode latitude beginning data into a binary format digital - Reduce the size of digital source data Channel Coding - Code data for transmition over a noisy discourse channel Increases size of data digital - add surplusage to identify and remedy errors Analog - represent digital set by analog signals Complete Information Theory was develop 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 Input and outfit images are digital Output image must be littler (i.e. ? 500 kBytes) This is a digital source steganography problem C. A. Bouman: Digital Image Processing - January 7, 2007 3 Two Types of Source (Image) Coding lossless co de (entropy cryptanalytics) Data can be decoded to form on the nose the same bits employ in zero flock solely achieve moderate condensing (e.g. 2:1 3:1) for natural images Can be authoritative in definite applications such as medical tomography 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 permit X be a ergodic variables pickings determine 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 Conditional Entropy and uncouth 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: BestEssayCheap.com

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