e178-L12.ppt

e178-L12.ppt - IMAGE COMPRESSION- I Week VIII Image...

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Image Compression-I 1 IMAGE COMPRESSION- I Week VIII Image Compression-I 2 Reading. . Chapter 8 Section 8.1 8.2.1 Huffman, 8.2.5 run-length 8.2.8 Block transform coding & JPEG 8.2.9 Predictive coding--spatial & temporal,, lossless and lossy 8.2.10 wavelet compression
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Image Compression-I 3 Image compression Objective: To reduce the amount of data required to represent an image. Important in data storage and transmission • Progressive transmission of images (internet, www) • Video coding (HDTV, Teleconferencing, digital cinema) • Digital libraries and image databases •Medical imaging •Satellite images Image Compression-I 4 IMAGE COMPRESSION Data redundancy Self-information and Entropy Error-free and lossy compression Huffman coding Predictive coding Transform coding
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Image Compression-I 5 Lossy vs Lossless Compression Compression techniques Information preserving Lossy (loss-less) Images can be compressed and restored without any loss of information. Application: Medical images, GIS Perfect recovery is not possible but provides a large data compression. Example : TV signals, teleconferencing Image Compression-I 6 Data Redundancy CODING : Fewer bits to represent frequent symbols. INTERPIXEL / INTERFRAME : Neighboring pixels have similar values. PSYCHOVISUAL : Human visual system can not simultaneously distinguish all colors.
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Image Compression-I 7 Coding Redundancy Fewer number of bits to represent frequently occurring symbols. Let p r (r k ) = n k / n, k = 0,1,2, . ., L-1; L # of gray levels. Let r k be represented by l (r k ) bits. Therefore average # of bits required to represent each pixel is L l r p r A avg k k L r k = = ( ) ( ) ( ) 0 1 Usually l(r k ) = m bits (constant).
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e178-L12.ppt - IMAGE COMPRESSION- I Week VIII Image...

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