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Books on the topic 'Quantization technique'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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1

So, Kevin Kin Man. A new quantization technique for linear predictive speech coding. University of Manchester, 1994.

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2

Garcia-Arellano, Christian M. Quantization techniques for similarity search in high-dimensional data spaces. National Library of Canada, 2002.

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3

Norbury, John W. Second quantization techniques in the scattering of nonidentical composite bodies. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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4

C, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Conditional entropy-constrained residual VQ with application to image coding. National Aeronautics and Space Administration, 1996.

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5

C, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Conditional entropy-constrained residual VQ with application to image coding. National Aeronautics and Space Administration, 1996.

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6

C, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Conditional entropy-constrained residual VQ with application to image coding. National Aeronautics and Space Administration, 1996.

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7

Vector quantization. IEEE Press, 1990.

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8

Parthasarathy, Priya. Optimum quantization for the adaptive loops in MDFE. 1997.

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9

Stoft, David E. Adaptive subband quantization for low bit-rate lossy image compression. 1994.

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10

Sogge, Christopher D. Stationary phase and microlocal analysis. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691160757.003.0004.

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This chapter discusses basic techniques from the theory of stationary phase. After giving an overview of the method of stationary phase, the chapter moves on to a discussion of pseudodifferential operators, by going over the basics from the calculus of pseudodifferential operators and their various microlocal properties, in the process obtaining an equivalent definition of wave front sets, before defining pseudodifferential operators on manifolds and going over some of their properties. The chapter then lays out the propagation of singularities as well as Egorov's theorem, which involves conju
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11

Conditional entropy-constrained residual VQ with application to image coding. National Aeronautics and Space Administration, 1996.

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12

Klette, Reinhard, and Azriel Rosenfeld. Digital Geometry: Geometric Methods for Digital Picture Analysis. Elsevier Science & Technology Books, 2004.

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13

Perlmutter, Arnold, Stephan L. Mintz, and Behram N. Kursunogammalu. Neutrino Mass, Dark Matter, Gravitational Waves, Monopole Condensation, and Light Cone Quantization. Springer London, Limited, 2013.

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14

Neutrino Mass, Dark Matter, Gravitational Waves, Monopole Condensation, and Light Cone Quantization. Springer, 2013.

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15

Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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