This paper recommends techniques to use for data compression of video data used to point a telescope and from a camera observing a robot, for transmission from the proposed U.S. Space Station to Earth. The mathematical basis of data compression is presented, followed by a general review of data compression techniques. A technique that has wide-spread use in data compression of videoconferencing images is recommended for the robot observation data. Bit rates of 60 to 400 kbits/sec can be achieved. Several techniques are modelled to find a best technique for the telescope data. Actual starfield images are used for the evaluation. The best technique is chosen on the basis of which model provides the most compression while preserving the important information in the images. Compression from 8 bits per pel to 0.015 bits per pel is achieved.

Thesis (Ph.D.)--University of Wyoming, 2007. / Title from PDF title page (viewed on June 17, 2009). Includes bibliographical references (p. 133-139).

A multispectral image data compression scheme has been investigated in which a scene is imaged onto a detector array whose elements vary in spectral sensitivity. The elements are staggered such that the scene is undersampled within any single spectral band, but is sufficiently sampled by the total array. Compression thus results from transmitting only one spectral component of a scene at any given array coordinate. The pixels of the mosaic array may then be directly transmitted via PCM or undergo further compression (e.g. DPCM). The scheme has the advantages of attaining moderate compression without compression hardware at the transmitter, high compression with low-order DPCM processing, and a choice of reconstruction algorithms suitable to the application at hand. Efficient spatial interpolators such as parametric cubic convolution may be employed to fill in the missing pixels in each spectral band in cases where high resolution is not a requirement. However, high-resolution reconstructions are achieved by a space-variant minimum-mean-square spectral regression estimation of the missing pixels of each band from the adjacent samples of other bands. In this case, reconstruction accuracy is determined by the local spectral correlations between bands, the estimates of which include the effects of interband contrast reversal. Digital simulations have been performed on three-band aerial and four-band Landsat multispectral images. Spectral regressions of mosaic array data can provide reconstruction errors comparable to second-order DPCM processing and lower than common intraband interpolators at data rates of approximately 2 bits per pixel. When the mosaic data is itself DPCM-coded, the radiometric accuracy of spectral regression is superior to direct DPCM for equivalent bit rates.

by Chiu Kim Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 103-108). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Speech production --- p.2 / Chapter 1.2 --- Low bit rate speech coding --- p.4 / Chapter Chapter 2 --- Speech analysis & synthesis --- p.8 / Chapter 2.1 --- Linear prediction of speech signal --- p.8 / Chapter 2.2 --- LPC vocoder --- p.11 / Chapter 2.2.1 --- Pitch and voiced/unvoiced decision --- p.11 / Chapter 2.2.2 --- Spectral envelope representation --- p.15 / Chapter 2.3 --- Excitation --- p.16 / Chapter 2.3.1 --- Regular pulse excitation and Multipulse excitation --- p.16 / Chapter 2.3.2 --- Coded excitation and vector sum excitation --- p.19 / Chapter 2.4 --- Multiband excitation --- p.22 / Chapter 2.5 --- Multiband excitation vocoder --- p.25 / Chapter Chapter 3 --- Dual-band and Quad-band excitation --- p.31 / Chapter 3.1 --- Dual-band excitation --- p.31 / Chapter 3.2 --- Quad-band excitation --- p.37 / Chapter 3.3 --- Parameters determination --- p.41 / Chapter 3.3.1 --- Pitch detection --- p.41 / Chapter 3.3.2 --- Voiced/unvoiced pattern generation --- p.43 / Chapter 3.4 --- Excitation generation --- p.47 / Chapter Chapter 4 --- A low bit rate Quad-Band Excitation LSP Vocoder --- p.51 / Chapter 4.1 --- Architecture of QBELSP vocoder --- p.51 / Chapter 4.2 --- Coding of excitation parameters --- p.58 / Chapter 4.2.1 --- Coding of pitch value --- p.58 / Chapter 4.2.2 --- Coding of voiced/unvoiced pattern --- p.60 / Chapter 4.3 --- Spectral envelope estimation and coding --- p.62 / Chapter 4.3.1 --- Spectral envelope & the gain value --- p.62 / Chapter 4.3.2 --- Line Spectral Pairs (LSP) --- p.63 / Chapter 4.3.3 --- Coding of LSP frequencies --- p.68 / Chapter 4.3.4 --- Coding of gain value --- p.77 / Chapter Chapter 5 --- Performance evaluation --- p.80 / Chapter 5.1 --- Spectral analysis --- p.80 / Chapter 5.2 --- Subjective listening test --- p.93 / Chapter 5.2.1 --- Mean Opinion Score (MOS) --- p.93 / Chapter 5.2.2 --- Diagnostic Rhyme Test (DRT) --- p.96 / Chapter Chapter 6 --- Conclusions and discussions --- p.99 / References --- p.103 / Appendix A Subroutine of pitch detection --- p.A-I - A-III / Appendix B Subroutine of voiced/unvoiced decision --- p.B-I - B-V / Appendix C Subroutine of LPC coefficients calculation using Durbin's recursive method --- p.C-I - C-II / Appendix D Subroutine of LSP calculation using Chebyshev Polynomials --- p.D-I - D-III / Appendix E Single syllable word pairs for Diagnostic Rhyme Test --- p.E-I

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Thesis (Ph.D.) -- McMaster University, 1997. / Includes bibliographical references [p. 157-164). Also available via World Wide Web.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [84]-91).

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [158]-166).