Optimal multiple description and multiresolution scalar quantizer design

Effros, Michelle

doi:10.1109/ita.2008.4601027

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…(For more details on interpretations and motivations of this MDSQ model, see, e.g., [3], [2], [4], [5], [6]. The term for I = ∅ is omitted since it does not affect the optimal design of MDSQ.)…”

Section: B Multiple Description and Multiresolution Scalar Quantizersmentioning

confidence: 99%

“…Although there are some MDSQ algorithms also for nonconvex cases (e.g., see [7], [8], [4]), several papers propose algorithms for convex MDSQs, and most MRSQ algorithms address the problem of finding the optimal one among convex MRSQs, that is, they hence lead to overall optimality if there is a convex optimal MDSQ/MRSQ (e.g., see [2], [9], [10], [11], [5], [12], [6], [13]). Under this constraint, optimal SQ design is equivalent to designing the optimal threshold sequence and corresponding codewords, so the search space is usually reduced significantly.…”

Section: Convexity Of Mdsqs/mrsqsmentioning

confidence: 99%

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On Codecell Convexity of Optimal Multiresolution Scalar Quantizers for Continuous Sources

Antos

2012

IEEE Trans. Inform. Theory

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Abstract-It has been shown by earlier results that for fixed rate multiresolution scalar quantizers and for mean squared error distortion measure, codecell convexity precludes optimality for certain discrete sources. However it was unknown whether the same phenomenon can occur for any continuous source. In this paper, examples of continuous sources (even with bounded continuous densities) are presented for which optimal fixed rate multiresolution scalar quantizers cannot have only convex codecells, proving that codecell convexity precludes optimality also for such regular sources.

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Section: B Multiple Description and Multiresolution Scalar Quantizersmentioning

confidence: 99%

Section: Convexity Of Mdsqs/mrsqsmentioning

confidence: 99%

On Codecell Convexity of Optimal Multiresolution Scalar Quantizers for Continuous Sources

Antos

2012

IEEE Trans. Inform. Theory

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“…The additional bit in expression (17) determines which quantizer is used in the coding process. This information is necessary for decoding.…”

Section: Two Companding Quantizers Vlc Modelmentioning

confidence: 99%

“…Compression algorithm for Laplacian source, consisting of an optimal bounded companding quantizer and simple lossless coder is given in [16]. Multi-resolution scalar quantizers are described in [17].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of variable-length code for data compression of memoryless Laplacian source

2011

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In this paper we present the efficient technique for compression and coding of memoryless Laplacian sources, which uses variable-length code (V LC). That technique is based on combination of two companding quantizers in the first case, and three companding quantizers in the second case. These quantizers have disjoint support regions, different number of representation levels and different compressor functions. The closed-form expressions are obtained for the distortion, average bit rate and signal to quantization noise ratio (SQN R). Presented numerical results point out the effects of rate-distortion (R-D) optimization on the system performances. Since our model assumes the general case of Laplacian distribution, it has a wide applications like coding of speech and images. It is shown that the difference of SQN R of our model and classical

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Network-aware Source Coding and Communication

Sarshar

Wang

et al. 2011

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An introduction to the theory and techniques for achieving high quality network communication with the best possible bandwidth economy, this book focuses on network information flow with fidelity. Covering both lossless and lossy source reconstruction, it is illustrated throughout with real-world applications, including sensor networks and multimedia communications. Practical algorithms are presented, developing novel techniques for tackling design problems in joint network-source coding via collaborative multiple description coding, progressive coding, diversity routing and network coding. With systematic introductions to the basic theories of distributed source coding, network coding and multiple description coding, this is an ideal self-contained resource for researchers and students in information theory and network theory.

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Optimal multiple description and multiresolution scalar quantizer design

Cited by 4 publications

References 14 publications

On Codecell Convexity of Optimal Multiresolution Scalar Quantizers for Continuous Sources

On Codecell Convexity of Optimal Multiresolution Scalar Quantizers for Continuous Sources

Optimisation of variable-length code for data compression of memoryless Laplacian source

Network-aware Source Coding and Communication

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