Design of distributed channel optimized multiple description vector quantizer

Valipour, Mehrdad; Lahouti, Farshad

doi:10.1109/bsc.2008.4563270

Cited by 2 publications

(5 citation statements)

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“…Note that here the quantizer is a Lloyd-Max quantizer adapted to the pdf of the distribution of X and not optimized for p Z . The SI is only taken into account in the inverse quantization step (see (13)). This explains the fact that when the CSNR is low, the SNR performance of the side decoder without the SI for d = 0 is slightly better than the SNR with SI, but gets worse when the CSNR increases.…”

Section: Resultsmentioning

confidence: 99%

“…Since the transmitted descriptions are decoded bit-by-bit, the central decoder may generate invalid indexes corresponding to the empty cells of index assignment. When that happens, all the quantization intervals in the row and column indicated by the two indexes are used in (13). The number of points on each curve corresponds to the number of bits needed to represent the indexes (5 for WZC and d = 0, 4 for d = 1, 3 for d = 2).…”

Section: Cross-decoding Of Multiple Descriptions With Simentioning

confidence: 99%

“…Recently, in [13], a deterministic annealing [14] approach was described for optimal design of multiple description vector quantizer with SI available at the decoder. The performance of the quantizer over channels subject to noise and packet loss was investigated and compared with the RD bound.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple Description Coding with Side Information: Practical Scheme and Iterative Decoding

Crave

Guillemot

Pesquet‐Popescu

2009

EURASIP J. Adv. Signal Process.

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Multiple description coding (MDC) with side information (SI) at the receiver is particularly relevant for robust transmission in sensor networks where correlated data is being transmitted to a common receiver, as well as for robust video compression. The rate-distortion region for this problem has been established in (Vaishampayan 1993). Here, we focus on the design of a practical MDC scheme with SI at the receiver. It builds upon both MDC principles and Slepian-Wolf (SW) coding principles. The input source is first quantized with a multiple description scalar quantizer (MDSQ) which introduces redundancy or correlation in the transmitted streams in order to take advantage of the path diversity. The resulting sequences of indexes are SW encoded, that is, separately encoded and jointly decoded. While the first step (MDSQ) plays the role of a channel code the second one (SW coding) plays the role of a source code, compressing the sequences of quantized indexes. In a second step, the cross-decoding of the two descriptions is proposed. This allows us to account for both the correlation with the SI as well as the correlation between the two descriptions.

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Section: Resultsmentioning

confidence: 99%

Section: Cross-decoding Of Multiple Descriptions With Simentioning

confidence: 99%

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Multiple Description Coding with Side Information: Practical Scheme and Iterative Decoding

Crave

Guillemot

Pesquet‐Popescu

2009

EURASIP J. Adv. Signal Process.

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“…Where and ,…, are the Lagrangian multipliers. We next set the derivative of (23), with respect to , equal to zero and consider the first constraints in (22) to obtain…”

Section: Source Encoder Designmentioning

confidence: 99%

“…In a scheme suggested in [22], in addition to two descriptions of a source sample, extra information generated by a WZ encoder, is transmitted for enhanced reconstruction in presence of packet loss. The authors consider the design of multiple description vector quantization (MDVQ) with SI available at the decoder in [23]. In [24], a MD with SI is considered using a non-optimized MDVQ and parity-based turbo codes as the Slepian-Wolf encoders over noiseless channels.…”

Section: Introductionmentioning

confidence: 99%

Channel Optimized Distributed Multiple Description Coding

Valipour

Lahouti

2012

IEEE Trans. Signal Process.

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In this paper, channel optimized distributed multiple description vector quantization (CDMD) schemes are presented for distributed source coding in symmetric and asymmetric settings. The CDMD encoder is designed using a deterministic annealing approach over noisy channels with packet loss. A minimum mean squared error asymmetric CDMD decoder is proposed for effective reconstruction of a source, utilizing the side information (SI) and its corresponding received descriptions. The proposed iterative symmetric CDMD decoder jointly reconstructs the symbols of multiple correlated sources. Two types of symmetric CDMD decoders, namely the estimated-SI and the soft-SI decoders, are presented which respectively exploit the reconstructed symbols and a posteriori probabilities of other sources as SI in iterations. In a multiple source CDMD setting, for reconstruction of a source, three methods are proposed to select another source as its SI during the decoding. The methods operate based on minimum physical distance (in a wireless sensor network setting), maximum mutual information and minimum end-to-end distortion. The performance of the proposed systems and algorithms are evaluated and compared in detail.EDICS: SEN-DCSC Distributed channel and source coding 2

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Design of distributed channel optimized multiple description vector quantizer

Cited by 2 publications

References 13 publications

Multiple Description Coding with Side Information: Practical Scheme and Iterative Decoding

Multiple Description Coding with Side Information: Practical Scheme and Iterative Decoding

Channel Optimized Distributed Multiple Description Coding

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