Image_Compression-2011

Image_Compression-2011 - IMAGE COMPRESSION Why Do We Need...

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IMAGE COMPRESSION
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Why Do We Need Compression? Requirements may outstrip the anticipated increase of storage space and bandwidth For data storage and data transmission DVD Video conference Printer The bit rate of uncompressed digital cinema data exceeds 1 Gbps Videostream
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Why Can We Compress? Spatial redundancy Neighboring pixels are not independent but correlated Temporal redundancy
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(Bandwidth Compression vs. Bit Rate Reduction) Reduction of the number of bits needed to represent a given image or it’s information Image Compression Image compression exploits the fact that all images are not equally likely Exploits energy gaps in signal
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Information vs Data REDUNDANT DATA INFORMATION DATA = INFORMATION + REDUNDANT DATA
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An Image Model- Ref: J.B.O’Neal Picture size is one unit wide by one unit high Width 1 Unit Height 1 Unit M 1/2 M 1/2 D M=Number of Samples D=Spacing Between Samples = Correlation Between Adjacent Samples
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Compression As It Relates To Image Content Picture Correlation Distance •Portrait 6.3 (Fills 1/2 Frame) •Typical 16.7 (Moderate Detail) •100 People 50 •2000 People 150 -1
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INTERFRAME and INTRAFRAME PROCESSING Interframe Processing Predictive Encoding Point to Point Line to Line Intraframe Processing
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BIT RATE = NQF N = NUMBER OF PIXELS Q = QUANTIZATION BITS/PIXEL F = FRAME RATE Compression Ratio = 10 LOG Channel Bit Rate N Q F
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REDUCING CREATES N Reduced Resolution F Image Blur Q Contouring (Artifacts) We need More Sophisticated Approaches
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Goal Store image data as efficiently as possible Ideally, want to Maximize image quality Minimize storage space and processing resources Can’t have best of both worlds What are some good compromises?
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Digital vs. Analog Modern image processing is done in digital domain If we have an analog source image, convert it to digital format before doing any processing
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Two main schools of image compression Lossless Stored image data can reproduce original image exactly Takes more storage space Uses entropy coding only (or none at all) Examples: BMP, TIFF, GIF Lossy Stored image data can reproduce something that looks “close” to the original image Uses both quantization and entropy coding Usually involves transform into frequency or other domain Examples: JPEG, JPEG-2000
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