lecture9_lossless_coding

lecture9_lossless_coding - Lossless Image Compression Yao...

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Lossless Image Compression Yao Wang Polytechnic Institute of NYU, Brooklyn, NY 11201 With contribution from Zhu Liu Partly based on A. K. Jain, Fundamentals of Digital Image Processing
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Yao Wang, NYU-Poly EL5123: Lossless Compression 2 Lecture Outline Introduction: need for compression Review of basic probability theory Binary encoding Fixed length coding Variable length coding Huffman coding Other variable length code (LZW, arithmetic) Runlength coding of bilevel images Fax coding standard Lossless Predictive coding Coding block diagram Design of linear predictors
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Yao Wang, NYU-Poly EL5123: Lossless Compression 3 Necessity for Signal Compression Size One Page of Text 2 KB One 640x480 24-bit color still image 900 KB Voice ( 8 Khz, 8-bit) 8 KB /second Audio CD DA (44.1 Khz, 16-bit) 176 KB/second Animation ( 320x640 pixels, 16-bit color, 16 frame/s) 6.25 MB/second Video (720x480 pixels, 24-bit color, 30 frame/s) 29.7 MB/second Storage requirement for various uncompressed data types Goal of compression Given a bit rate, achieve the best quality Given an allowed distortion, minimize the data amount (Rate-Distortion or RD tradeoff)
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Yao Wang, NYU-Poly EL5123: Lossless Compression 4 Image Coding Standards by ITU and ISO G3,G4: facsimile standard JBIG: The next generation facsimile standard ISO Joint Bi-level Image experts Group JPEG: For coding still images or video frames. ISO Joint Photographic Experts Group JPEG2000: For coding still images, more efficient than JPEG Lossless JPEG: for medical and archiving applications. MPEGx: audio and video coding standards of ISo H.26x: video coding standard of ITU-T ITU: International telecommunications union ISO: International standards organization
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Yao Wang, NYU-Poly EL5123: Lossless Compression 5 A Typical Compression System Transfor- mation Quanti- zation Binary Encoding Prediction Transforms Model fitting …... Scalar Q Vector Q Fixed length Variable length (Huffman, arithmetic, LZW) Input Samples Transformed parameters Quantized parameters Binary bitstreams • Motivation for transformation --- To yield a more efficient representation of the original samples.
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Yao Wang, NYU-Poly EL5123: Lossless Compression 6 Review of Basic Probability Theory A. Papoulis and S. Pillai, “ Probability, Random Variables, and Stochastic Processes ”, McGraw- Hill, Inc., 2002. http://www.mhhe.com/engcs/electrical/papoulis/ Random Variable (RV)* The set, S, of all possible outcomes of a particular experiment is called the sample space for the experiment. An event is any collection of possible outcomes of an experiment, that is, any subset of S. – Random variable
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lecture9_lossless_coding - Lossless Image Compression Yao...

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