On index assignment and the design of multiple description quantizers

Yahampath, Pradeepa

doi:10.1109/icassp.2004.1326897

Cited by 17 publications

(13 citation statements)

References 5 publications

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“…Also MDPVQ and MDVQ are initialized in this way. As was discussed in [41], channeloptimized codebook training may also by itself yield good index assignments.…”

Section: A Prerequisitesmentioning

confidence: 98%

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On Multiple Description Coding of Sources with Memory

Persson

Eriksson

2010

IEEE Trans. Commun.

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Abstract-We propose a framework for multiple description coding (MDC) of sources with memory. A new source coding method for lossless transmission of correlated sources, power series quantization (PSQ), was recently suggested. PSQ uses a separate linear or non-linear predictor for each quantizer region, and has shown increased performance compared to several common quantization schemes for sources with memory. We propose multiple description PSQ as a special case within our framework. The suggested scheme is shown to increase performance compared with previous state-of-the-art MDC methods.Index Terms-Channel-optimized quantization, packet erasure channel, memory-based quantization.

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“…Also MDPVQ and MDVQ are initialized in this way. As was discussed in [41], channeloptimized codebook training may also by itself yield good index assignments.…”

Section: A Prerequisitesmentioning

confidence: 98%

“…Thereafter, the simulated annealing index assignment (IA) method [41], that has showed good performance in comparison with other schemes, is used. This IA is repeated for 0 = √ , 0.25 , 0.5 , 0.75 , and .…”

Section: A Prerequisitesmentioning

confidence: 99%

On Multiple Description Coding of Sources with Memory

Persson

Eriksson

2010

IEEE Trans. Commun.

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“…3. Perform pairwise swaps of the indexes in row n and, for each swap, calculate the cost c 2,n using (6). Apply the Hungarian algorithm to determine the optimal set of indicator variables {x2jn} that minimizes D2 n in (7).…”

Section: Index Assignment Algorithmmentioning

confidence: 99%

“…A similar approach for assigning the column index 62 is developed on a row-by-row basis. If row n is selected, the cost of moving the index from position (n, i) to (n, j) is given by C2,n Z Yx Acc/ Z YkP(I= k) 112p(I kcCC/ P(I =0( (6) where C} is the new set of indices found in the column j.…”

Section: Index Assignment Algorithmmentioning

confidence: 99%

“…Because of the nature of this problem is NP-hard, an exhaustive search is not feasible and many practical index assignment algorithms are suboptimal [5]- [8]. The index assignment problem of MDVQs have been considered in conjunction with binary switching algorithm in [5] and with simulating annealing algorithm in [6]. In attempts to apply fast solution approaches, we presented a different formulation of the index assignment problem on the basis of a linear programming framework [7][9].…”

Section: Introductionmentioning

confidence: 99%

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Multiple Description Quantization for Recognizing Voice Over Packet Networks

Lin

Chen

et al. 2006

2006 First International Conference on Communications and Networking in China

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The practical design ofmultiple description vector quantizers for robust distributed speech recognition over packet networks is investigated. In the proposed system, speech parameters are quantized and mapped to multiple descriptions for transmission over independent channels. A new approach to the index assignment optimization is presented on the basis of a linear programming framework. Also, a fast local search algorithm is proposed to find the optimal index assignment without compromising the speech recognition accuracy. Experiments with random packet loss in a range of loss conditions are conducted on the Mandarin digit string recognition task. Simulation results indicate that the proposed multiple description scheme provides more robust performance than the ETSI standardized split vector quantization scheme with a single description.

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