UW CSEP 590 - Data Compression (30 pages)

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Data Compression



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Data Compression

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Pages:
30
School:
University of Washington
Course:
Csep 590 - Special Topics In Computer Science (PMP)
Special Topics In Computer Science (PMP) Documents

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CSEP 590 Data Compression Autumn 2007 Context Based Arithmetic Coding for the DCT CBACD Kyle Littlefield 2006 CSEP 590 Lecture 10 Autumn 2007 1 CBACD overview Evolved out of PACW A simple wavelet based coder developed at UW by Dane Barney and Amanda Askew Goals Replace wavelet transform with the DCT Replace context model with one suitable for the DCT Compare performance to Existing DCT based methods primarily JPEG State of the art wavelet methods CSEP 590 Lecture 10 Autumn 2007 2 CBACD Overview Results 36 34 PSNR dB 32 30 28 26 CBACD JPEG 24 Performs significantly better than JPEG Performs slightly under wavelet based methods such as SPIHT and JPEG 2000 JPEG 2000 SPIHT 22 0 0 2 0 4 0 6 0 8 1 Bit Rate bpp CSEP 590 Lecture 10 Autumn 2007 3 CBACD Overview Encoder original image significance pass split into bit planes DCT bit planes context modeling refinement pass header data normalize normalization constant coefficients DC average Decoder reconstructed image recombine bit planes inverse DCT add DC average significance pass bit planes extrapolate coefficients to bit planes that were not encoded header data encoded bit stream 1001111011 subtract DC average arithmetic coding denormalize coefficients arithmetic coding context modeling refinement pass normalization constant DC average CSEP 590 Lecture 10 Autumn 2007 4 Context Modeling Significance Bits Based on two factors Intra block correlation Relationships between subbands within a block Inter block correlation Relationships to neighboring blocks within the same subband Coefficient of interest Intra block inter subband coefficients Inter block intra subband coefficients For which information from the current bit plane is available Inter block intra subband coefficients For which only information from the previous bit plane is available CSEP 590 Lecture 10 Autumn 2007 5 Context Modeling Significance Bits First a significance factor is computed based on a linear sum of the two factors 63 isSig sub x i y j f sub x y



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