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Finite-state-vector quantization for waveform coding
J. Foster,Robert M. Gray,M. Dunham +2 more
- 01 Jan 1982
288
TL;DR: Finite-state vector quantizers are designed and simulated for Gauss-Markov sources and sampled speech data, and the resulting performance and storage requirements are compared with ordinary memoryless vector quantization.
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Abstract: A finite-state vector quantizer is a finite-state machine used for data compression: Each successive source vector is encoded into a codeword using a minimum distortion rule, and into a code book, depending on the encoder state. The current state and the selected codeword then determine the next encoder state. A finite-state vector quantizer is capable of making better use of the memory in a source than is an ordinary memoryless vector quantizer of the same dimension or blocklength. Design techniques are introduced for finite-state vector quantizers that combine ad hoc algorithms with an algorithm for the design of memoryless vector quantizers. Finite-state vector quantizers are designed and simulated for Gauss-Markov sources and sampled speech data, and the resulting performance and storage requirements are compared with ordinary memoryless vector quantization.
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Citations
Quantization
Robert M. Gray,David L. Neuhoff +1 more
TL;DR: The key to a successful quantization is the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.
2.1K
Image coding using vector quantization: a review
Nasser M. Nasrabadi,R.A. King +1 more
TL;DR: First, the concept of vector quantization is introduced, then its application to digital images is explained, and the emphasis is on the usefulness of the vector quantification when it is combined with conventional image coding techniques, orWhen it is used in different domains.
1.1K
Vector quantization in speech coding
John Makhoul,S. Roucos,H. Gish +2 more
- 01 Nov 1985
TL;DR: This tutorial review presents the basic concepts employed in vector quantization and gives a realistic assessment of its benefits and costs when compared to scalar quantization, and focuses primarily on the coding of speech signals and parameters.
1K
Statistical-model-based speech enhancement systems
Yariv Ephraim
- 01 Oct 1992
TL;DR: A unified statistical approach for the three basic problems of speech enhancement is developed, using composite source models for the signal and noise and a fairly large set of distortion measures.
415
Space-frequency quantization for wavelet image coding
Zixiang Xiong,Kannan Ramchandran,Michael T. Orchard +2 more
- 14 Nov 1996
TL;DR: The resulting wavelet packet coder offers a universal transform coding framework within the constraints of filter bank structures by allowing joint transform and quantizer design without assuming a priori statistics of the input image.
332
References
•Book
Introduction to Automata Theory, Languages, and Computation
John E. Hopcroft,Rajeev Motwani,Rotwani,Jeffrey D. Ullman +3 more
- 01 Jan 1979
TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
14.5K
An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
•Book
Finite-state-vector quantization for waveform coding
J. Foster,Robert M. Gray,M. Dunham +2 more
- 01 Jan 1982
TL;DR: Finite-state vector quantizers are designed and simulated for Gauss-Markov sources and sampled speech data, and the resulting performance and storage requirements are compared with ordinary memoryless vector quantization.
290
Vector quantization of speech and speech-like waveforms
TL;DR: A comparison of the results for the real speech and the simulated speech provides a quantitative measure of the accuracy of such models and, hence, of the applicability of information theory bounds and code designs based on probabilistic models.
214
An Algorithm for the Design of Labeled-Transition Finite-State Vector Quantizers
M. Dunham,Robert M. Gray +1 more
TL;DR: The algorithm for FSVQ design for waveform coders is extended to FSVZ design of linear predictive coded (LPC) speech parameter vectors using an Itakura-Saito distortion measure and a new technique for the iterative improvement of the next-state function based on an algorithm from adaptive stochastic automata theory is introduced.
114