On Finite-State Vector Quantization for Noisy Channels

Yahampath, Pradeepa; Pawlak, M.

doi:10.1109/tcomm.2004.838736

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…FSVQ is a VQ with a time-varying encoder and decoder pair [21], which is realized by means of a finite-state machine. Assume that a FSVQ contains M distinct states, S 1 , .…”

Section: Finite-state Vector Quantizationmentioning

confidence: 99%

“…FSVQ [19,20], which incorporates memory into a memoryless VQ, is intrinsically a prediction-based technique. An FSVQ can be regarded as a finite-state machine [21], which contains multiple states, each corresponding to a certain state codebook. The state transition is determined by a next-state function based on the information obtained from the previously encoded vectors.…”

Section: Introductionmentioning

confidence: 99%

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A memory efficient finite-state source coding algorithm for audio MDCT coefficients

Jiang

Yin

Liu

2014

J AUDIO SPEECH MUSIC PROC.

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To achieve a better trade-off between the vector dimension and the memory requirements of a vector quantizer (VQ), an entropy-constrained VQ (ECVQ) scheme with finite memory, called finite-state ECVQ (FS-ECVQ), is presented in this paper. The scheme consists of a finite-state VQ (FSVQ) and multiple component ECVQs. By utilizing the FSVQ, the inter-frame dependencies within source sequence can be effectively exploited and no side information needs to be transmitted. By employing the ECVQs, the total memory requirements of the FS-ECVQ can be efficiently decreased while the coding performance is improved. An FS-ECVQ, designed for the modified discrete cosine transform (MDCT) coefficients coding, was implemented and evaluated based on the Unified Speech and Audio Coding (USAC) scheme. Results showed that the FS-ECVQ achieved a reduction of the total memory requirements by about 11.3%, compared with the encoder in USAC final version (FINAL), while maintaining a similar coding performance.

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“…FSVQ is a VQ with a time-varying encoder and decoder pair [21], which is realized by means of a finite-state machine. Assume that a FSVQ contains M distinct states, S 1 , .…”

Section: Finite-state Vector Quantizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A memory efficient finite-state source coding algorithm for audio MDCT coefficients

Jiang

Yin

Liu

2014

J AUDIO SPEECH MUSIC PROC.

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“…A design method for predictive trellis coding was proposed in [22], and later extended to combine trellis-coded modulation and quantization in [23]. Finite-state vector quantization for a noisy channel was proposed in [24], [25], and has recently been treated in [26]. Safety-net FSVQ, that works by applying two codebooks for quantization, one state-dependent, and one state-independent, was suggested in [11].…”

Section: A Previous Effortsmentioning

confidence: 99%

Power series quantization for noisy channels

Persson

Eriksson

2010

IEEE Trans. Commun.

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Abstract-A recently proposed method for transmission of correlated sources under noise-free conditions, power series quantization (PSQ), uses a separate linear or nonlinear predictor for each quantizer region, and has shown to increase performance compared to several common quantization schemes for sources with memory. In this paper, it is shown how to apply PSQ for transmission of a source with memory over a noisy channel.A channel-optimized PSQ (COPSQ) encoder and codebook optimization algorithms are derived. The suggested scheme is shown to increase performance compared with previous state-ofthe-art methods.Index Terms-Channel-optimized quantization, noisy channels, memory-based quantization, spectrum coding.

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“…In a related work [27], we consider the design of finitestate vector quantisers (FSVQ) for a memoryless noisy channel. In an FSVQ, both the encoder and decoder are finite-state machines, and the quantised output at a given time instant depends on both the input and the system state.…”

Section: Optimal Soft-decodingmentioning

confidence: 99%

“…The next-state of the system is a deterministic function (chosen as a part of the quantiser design procedure) of the current state and the input. The decoding problem in Reference [27] is conceptually similar to the problem considered here, in that, the goal is to estimate a random variable (source vector) associated with the unobserved state of some finite-state process, based on noisy observations. However, since an FSVQ is essentially a predictive quantiser, a sequence of transmitted codewords is nearly uncorrelated and cannot be considered as a Markov process.…”

Section: Optimal Soft-decodingmentioning

confidence: 99%

Vector quantisation for finite‐state Markov channels and application to wireless communications

Yahampath

Pawlak

2007

Trans Emerging Tel Tech

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SUMMARYVector quantisation for joint source-channel (JSC) coding over a finite-state Markov channel (FSMC) is studied. In particular, minimum mean square error (MMSE) decoding of a vector quantised source in the absence of channel state information (CSI) is considered. Based on hidden Markov modelling of the channel output, two decoding strategies are proposed. The first one is a soft-decoder which estimates the source reconstruction vectors directly from a sequence of channel output samples. The second one is a harddecoder based on joint maximum a posteriori (MAP) probability estimation of channel symbols and channel states. An iterative procedure for designing JSC optimised vector quantisers (VQs) is also proposed. Finally, the design of VQs for wireless channels using a finite-state model is examined. Experimental results are provided for a Gauss-Markov source and flat-fading wireless channels. A comparison with an idealised tandem source-channel coding system is also provided to demonstrate the advantage of the proposed JSC coding approach.

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On Finite-State Vector Quantization for Noisy Channels

Cited by 9 publications

References 25 publications

A memory efficient finite-state source coding algorithm for audio MDCT coefficients

A memory efficient finite-state source coding algorithm for audio MDCT coefficients

Power series quantization for noisy channels

Vector quantisation for finite‐state Markov channels and application to wireless communications

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