By William A. Pearlman
With transparent and easy-to-understand reasons, this e-book covers the basic innovations and coding tools of sign compression, when nonetheless protecting technical intensity and rigor. It features a wealth of illustrations, step by step descriptions of algorithms, examples and perform difficulties, which make it an incredible textbook for senior undergraduate and graduate scholars, in addition to an invaluable self-study device for researchers and pros. rules of lossless compression are coated, as are numerous entropy coding options, together with Huffman coding, mathematics coding and Lempel-Ziv coding. Scalar and vector quantization and trellis coding are completely defined, and a whole bankruptcy is dedicated to mathematical adjustments together with the KLT, DCT and wavelet transforms. The workings of remodel and subband/wavelet coding structures, together with JPEG2000 and SBHP photo compression and H.264/AVC video compression, are defined and a distinct bankruptcy is supplied on set partition coding, laying off new mild on SPIHT, SPECK, EZW and similar methods
''With transparent and easy-to-understand causes, this ebook covers the elemental strategies and coding tools of sign compression, when nonetheless maintaining technical intensity and rigor. It includes a wealth of illustrations, step by step descriptions of algorithms, examples and perform difficulties, which make it a fantastic textbook for senior undergraduate and graduate scholars, in addition to an invaluable self-study instrument for researchers and pros. rules of lossless compression are lined, as are a variety of entropy coding ideas, together with Huffman coding, mathematics coding and Lempel-Ziv coding. Scalar and vector quantization and trellis coding are completely defined, and a whole bankruptcy is dedicated to mathematical variations together with the KLT, DCT and wavelet transforms. The workings of rework and subband/wavelet coding platforms, together with JPEG2000 and SBHP photograph compression and H.264/AVC video compression, are defined and a special bankruptcy is supplied on set partition coding, laying off new gentle on SPIHT, SPECK, EZW and comparable methods''-- Read more... computing device generated contents be aware: 1. Motivation: the significance of compression; 2. publication evaluation; three. rules of lossless compression; four. Entropy coding strategies; five. Lossy compression of scalar assets; 6. Coding of assets with reminiscence; 7. Mathematical modifications; eight. price regulate in remodel coding platforms; nine. remodel coding structures; 10. Set partition coding; eleven. Subband/wavelet coding structures; 12. tools for lossless compression of pictures; thirteen. colour and multi-component snapshot and video coding; 14. dispensed resource coding
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Extra resources for Digital signal compression : principles and practice
Lossless coding of the subbands requires no bit allocation and gives perfect reconstruction of the source when reversible, fixed-precision filters are used for the subband transform. There is even a theoretical justification for coding gains in lossless coding, as it has been proved that a subband transform reduces the entropy of the source and therefore the rate for lossless coding . 4 Set partition coding One of the unique aspects of this book is a coherent exposition of the principles and methods of set partition coding in Chapter 10.
1. 8. S. Rao and W. A. Pearlman, “Analysis of linear prediction, coding, and spectral estimation from subbands,” IEEE Trans. Inf. Theory, vol. 42, no. 4, pp. 1160–1178, July 1996. 9. -B. Chai, J. Vass, and X. Zhuang, “Significance-linked connected component analysis for wavelet image coding,” IEEE Trans. , vol. 8, no. 6, pp. 774–784, June 1999. Further reading B. Girod, A. M. Aaron, S. Rane, and D. Rebollo-Monedero, “Distributed video coding,” Proc. IEEE, vol. 91, no. 1, pp. 71–83, Jan. 2005. E.
Pradhan, J. Kusuma, and K. Ramchandran, “Distributed compression in a dense microsensor network,” IEEE Signal Process. , pp. 51–60, Mar. 2002. A. Said and W. A. Pearlman, “A new, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. , vol. 6, no. 3, pp. 243–250, June 1996. J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. , vol. 41, no. 12, pp. 3445–3462, Dec. 1993. D. S. Taubman, “High performance scalable image compression with EBCOT,” IEEE Trans.