Institute for Systems Research Technical Reports
Permanent URI for this collectionhttp://hdl.handle.net/1903/4376
This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.
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Item Quantization of Memoryless and Gauss-Markov Sources Over Binary Markov Channels(1994) Phamdo, N.; Alajaji, Fady; Farvardin, Nariman; ISRJoint source-channel coding for stationary memoryless and Gauss- Markov sources and binary Markov channels is considered. The channel is an additive-noise channel where the noise process is an M-th order Markov chain. Two joint source-channel coding schemes are considered. The first is a channel-optimized vector quantizer - optimized for both source and channel. The second scheme consists of a scalar quantizer and a maximum a posteriori detector. In this scheme, it is assumed that the scalar quantizer output has residual redundancy that can be exploited by the maximum a posteriori detector to combat the correlated channel noise. These two schemes are then compared against two schemes which use channel interleaving. Numerical results show that the proposed schemes outperform the interleaving schemes. For very noisy channels with high noise correlation, gains of 4 to 5 dB in signal-to-noise ratio are possible.Item Detection of Binary Sources Over Discrete Channels with Additive Markov Noise(1994) Alajaji, Fady; Phamdo, N.; Farvardin, Nariman; Fuja, Tom E.; ISRWe consider the problem of directly transmitting a binary source with an inherent redundancy over a binary channel with additive stationary ergodic Markov noise. Out objective is to design an optimum receiver which fully utilizes the source redundancy in order to combat the channel noise.We investigate the problem of detecting a binary iid non-uniform source transmitted across the Markov channel. Two maximum a posteriori (MAP) formulations are considered: a sequence MAP detection and an instantaneous MAP detection. The two MAP detection problems are implemented using a modified version of the Viterbi decoding algorithm and a recursive algorithm. Necessary and sufficient conditions under which the sequence MAP detector becomes useless as well as simulation results are presented. A comparison between the performance of the proposed system with that of a (substantially more complex) traditional tandem source-channel coding scheme exhibits a better performance for the proposed scheme at relatively high channel bit error rates.
The same detection problem is then analyzed for the case of a binary symmetric Markov source. Analytical and simulation results show the existence of a "mismatch" between the source and the channel. This mismatch is reduced by the use of a rate-one convolutional encoder. Finally, the detection problem is generalized for the case of a binary non-symmetric Markov source.
Item Speech Coding over Noisy Channels(1994) Farvardin, Nariman; ISRThis chapter contains a discussion of quantization over noisy channels. The effects of channel noise on the performance of vector quantizers are discussed and algorithms for the design of noisy-channel vector quantizers are presented. It is argued that in certain practical situations where delay and complexity place hard limits on system parameters, a combined source-channel coding approach might be preferable to the more traditional tandem source-channel coding. Examples of full-searched, multi- stage and finite-state vector quantization designed for a noisy channel are provided for coding of speech line spectrum pair parameters.Item Trellis-Based Scalar-Vector Quantizer for Memoryless Sources(1992) Laroia, Rajiv; Farvardin, Nariman; ISRThis paper describes a structured vector quantization approach for stationary memoryless sources that combines the scalar-vector quantizer (SVQ) ideas (Laroia and Farvardin) with trellis coded quantization (Marcellin and Fischer). The resulting quantizer is called the trellis-based scalar-vector quantizer (TB-SVQ). The SVQ structure allows the TB-SVQ to realize a large boundary gain while the underlying trellis code enables it to achieve a significant portion of the total granular gain. For large block- lengths and powerful (possibly complex) trellis codes the TB-SVQ can, in principle, achieve the rate-distortion bound. As indicated by the results obtained here, even for reasonable block-lengths and relatively simple trellis codes, the TB-SVQ outperforms all other reasonable complexity fixed-rate quantizers.Item Low Bit-Rate Image Coding Using a Three-Component Image Model(1992) Ran, X.; Farvardin, Nariman; ISRIn this paper the use of a perceptually-motivated image model in the context of image compression is investigated. The model consists of a so-called primary component which contains the strong edge information of the image, a smooth component which represents the background slow-intensity variations and a texture component which contains the textures. The primary component, which is known to be perceptually important, is encoded separately by encoding the intensity and geometric information of the strong edge brim contours. Two alternatives for coding the smooth and texture components are studied: Entropy-coded adaptive DCT and entropy-coded subband coding. It is shown via extensive simulations that the proposed schemes, which can be thought of as a hybrid of waveform coding and featurebased coding techniques, result in both subjective and objective performance improvements over several other image coding schemes and, in particular, over the JPEG continuous-tone image compression standard. These improvements are especially noticeable at low bit rates. Furthermore, it is shown that a perceptual tuning based on the contrast-sensitivity of the human visual system can be used in the DCT-based scheme, which in conjunction with the 3- component model, leads to additional subjective performance improvements.Item Finite-State Vector Quantization for Noisy Channels(1992) Hussain, Yunus; Farvardin, Nariman; ISRUnder noiseless channel conditions and for sources with memory, finite-state vector quantizers (FSVQs) exhibit improvements over memoryless vector quantizers. It is shown, however, that in the presence of channel noise, the performance of the FSVQ degrades significantly. This suggests that for noisy channels, the FSVQ design problem needs to be reformulated by taking into account the channel noise. Using some of the developments in joint source-channel trellis coding, we describe two different methods leading to two types of noisy channel FSVQs. We show by means of simulations on the Gauss-Markov source and speech LSP parameters and for a binary symmetric channel that both schemes are fairly robust to channel noise. For the Gauss-Markov source, the proposed noisy channel FSVQs perform at least as well as or better than the channel-optimized VQ, while for speech LSP parameters, they lead to saving of 1.5-4 bits/frame over the channel-optimized VQ depending on the level of noise in the channel.Item Optimal Detection of Discrete Markov Sources Over Discrete Memoryless Channels - Applications to Combined Sources-Channel Coding(1992) Phamdo, N.; Farvardin, Nariman; ISRWe consider the problem of detecting a discrete Markov source which is transmitted across a discrete memoryless channel. The detection is based upon the maximum a posteriori (MAP) criterion which yields the minimum probability of error for a given observation. Two formulations of this problem are considered: (i) a sequence MAP detection in which the objective is to determine the most probable transmitted sequence given the observed sequence and (ii) an instantaneous MAP detection which is to determine the most probable transmitted symbol at time n given all the observations prior to and including time n. The solution to the first problem results in a "Viterbi-like" implementation of the MAP detector (with large delay) while the later problem results in a recursive (with no delay). For the special case of the binary symmetric Markov source and binary symmetric channel, simulation results are presented and an analysis of these two systems yields explicit critical channel bit error rates above which the MAP detectors become useful.Applications of the MAP detection problem in a combined source-channel coding system are considered. Here it is assumed that the source is highly correlated and that the source encoder (in our case, a vector quantizer (VQ) fails to remove all of the source redundancy. The remaining redundancy at the output of the source encoder is referred to as the "residual" redundancy. It is shown, through simulation, that the residual redundancy can be used by the MAP detectors to combat channel errors. For small block sizes, the proposed system beats Farvardin and Vaishampayan's channel- optimized VQ by wide margins. Finally, it is shown that the instantaneous MAP detector can be combined with the VQ decoder to form a minimum mean-squared error decoder. Simulation results are also given for this case.
Item Extension of the Fixed-Rate Structured Vector quantizer to Vector Sources(1991) Laroia, Rajiv; Farvardin, Nariman; ISRThe fixed-rate structured vector quantizer (SVQ) derived from a variable-length scalar quantizer was originally proposed for quantizing stationary memoryless sources. In this paper, the SVQ has been extended to a specific type of vector sources in which each component is a stationary memoryless scalar subsource in dependent of the other components. algorithms for the design and implementation of the original SVQ are modified to apply to this case. The resulting SVQ, referred to as the extended SVQ (ESVQ), is then used to quantize stationary sources with memory (with know autocorrelation function). This is done by first using a linear orthonormal block transformation, such as the Karhunen- Loeve transform, to decorrelate a block of source samples. The transform output vectors, which can be approximated as the output of an independent-component vector source, are then quantized using the ESVQ. Numerical results are presented for the quantization of first-order Gauss-Markov sources using this scheme. It is shown that ESVQ-based scheme performs very close to the entropy-coded transform quantization while maintaining a fixed-rate output and outperforms the fixed-rate scheme which uses scalar Lloyd-Marx quantization of the transform coefficients. Finally, it is shown that this scheme also performs better than implementable vector quantizers, specially at high rates.Item A United Approach to Tree-Structured and Multi-Stage Vector Quantization for Noisy Channels(1991) Phamdo, N.; Farvardin, Nariman; Moriya, T.; ISRVector quantization (VQ) is a powerful and effective scheme which is widely used in speech and image coding applications. Two basic problems can be associated with VQ: (i) its large encoding complexity, and (ii) its sensitivity to channel errors. These two problems have been independently studied in the past. In this paper, we examine these two problems jointly. Specifically, the performances of two low-complexity VQs-the tree-structured VQ (TSVQ) and the multi-stage VQ (MSVQ) - when used over noisy channels are analyzed. An algorithms is developed for the design of channel-matched TSVQ (CM-TSVQ) and channel-matched MSVQ (CM- MSVQ) under the squared-error criterion. Extensive numerical results are given for the memoryless Gaussian source and the Gauss-Markov source with correlation coefficient 0.9. Comparisons with the ordinary TSVQ and MSVQ designed for the noiseless channel show substantial improvements when the channel is very noisy. The CM-MSVQ, which can be regarded as a block- structured combined source-channel coding scheme, is then compared with a block-structured tandem source-channel coding scheme (with the same block length as the CM-MSVQ). For the Gauss-Markov source, the CM-MSVQ outperforms the tandem scheme in all cases which we have considered. Furthermore, it is demonstrated that the CM-MSVQ is fairly robust to channel mismatch.