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Source coding and graph entropies
IEEE Transactions on Information Theory, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Noga Alon, Alon Orlitsky
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Entropy-constrained trellis coded quantization
[1991] Proceedings. Data Compression Conference, 1992Summary: Trellis-coded quantization is generalized to allow noiseless coding of the trellis branch reproduction symbols. An entropy-constrained trellis- coded quantization (ECTCQ) design algorithm is presented, based on the generalized Lloyd algorithm for trellis code design and the entropy- constrained vector quantization design algorithm.
Thomas R. Fischer, Min Wang
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On entropy coded and entropy constrained lattice vector quantization
Proceedings of 3rd IEEE International Conference on Image Processing, 2002Two lattice vector quantization methods are compared. The first, classical method uses Dirichlet domains of lattice points as quantization cells and assigns them reconstruction vectors minimizing the distortion. The second method uses the lattice as codebook but modifies the shapes of the quantization cells by searching for each input vector the ...
Stephan F. Simon, Wolfgang Niehsen
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The entropy of a code with probabilities
ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202), 2002The entropy of a code with probabilities is defined and as a consequence the concept of conservation of entropy in lossless source coding emerges in a natural manner. For any given probability distribution (p/sub 1/,p/sub 2/,...,p/sub T/) all the distinct decompositions of the associated entropy function h(p/sub 1/,p/sub 2/,...,p/sub T/), as a function
V.C. Da Rocha, H.M. De Oliveira
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2010
Let us consider a 2-bit quantizer that represents quantized values using the following set of quantization indexes: f0; 1; 2; 3g: Each quantization index given above is called a source symbol, or simply a symbol, and the set is called a symbol set. When applied to quantize a sequence of input samples, the quantizer produces a sequence of quantization ...
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Let us consider a 2-bit quantizer that represents quantized values using the following set of quantization indexes: f0; 1; 2; 3g: Each quantization index given above is called a source symbol, or simply a symbol, and the set is called a symbol set. When applied to quantize a sequence of input samples, the quantizer produces a sequence of quantization ...
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Combination coding: a new entropy coding technique
Proceedings of Data Compression Conference - DCC '96, 1996Summary form only given. Entropy coding is defined to be the compression of a stream of symbols taken from a known symbol set where the probability of occurrence of any symbol from the set at any given point in the stream is constant and independent of any known occurrences of any other symbols.
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Improved entropy coding for component-based image coding
2011 18th IEEE International Conference on Image Processing, 2011In this paper, we improve on our previous work regarding component-based image coding, a hybrid transform-based/perceptual image coding scheme based on a decomposition of the image into structure and texture characterized by a Gaussian Markov random field.
Christian Feldmann, Johannes Ballé
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Concatenated error-correcting entropy codes and channel codes
IEEE International Conference on Communications, 2003. ICC '03., 2004We propose a general class of concatenated error-correcting entropy codes and channel codes. In this way we extend and generalize the existing body of work on iterative decoding of entropy and channel codes. Using the structure and properties of serial concatenated codes, we employ error-correcting entropy codes as the outer code, and a convolutional ...
Ahmadreza Hedayat, Aria Nosratinia
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2002
A binary digit, or “bit,” b, takes one of the values b = 0 or b = 1. A single bit has the ability to convey a certain amount of information — the information corresponding to the outcome of a binary decision, or “event,” such as a coin toss. If we have N bits, then we can identify the outcomes of N binary decisions.
David S. Taubman, Michael W. Marcellin
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A binary digit, or “bit,” b, takes one of the values b = 0 or b = 1. A single bit has the ability to convey a certain amount of information — the information corresponding to the outcome of a binary decision, or “event,” such as a coin toss. If we have N bits, then we can identify the outcomes of N binary decisions.
David S. Taubman, Michael W. Marcellin
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