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.
Browse
32 results
Search Results
Item Channel--Matched Hierarchical Table--Lookup Vector Quantization for Transmission of Video Over Wireless Channels(1997) Jafarkhani, Hamid; Farvardin, Nariman; ISRWe propose a channel-matched hierarchical table-lookup vector quantizer (CM-HTVQ) which provides some robustness against the channel noise. We use a finite-state channel to model slow fading channels and propose an adaptive coding scheme to transmit a source over wireless channels. The performance of CM-HTVQ is in general slightly inferior to that of, channel-optimized vector quantizer (COVQ) (the performances coincide at some cases); however, the encoder complexity of CM-HTVQ is much less than the encoder complexity of COVQ. A copy of this report has been published in the proceedings of The 1st Annual Advanced Telecommunications/Information Distribution Research Program Conference, January 21-22, 1997.Item Fast Reconstruction of Subband-Decomposed Progressively- Transmitted, Signals(1997) Jafarkhani, Hamid; Farvardin, Nariman; ISRIn this paper we propose a fast reconstruction method for a progressive subband-decomposed signal coding system. It is shown that unlike the, normal approach which contains a fixed computational complexity, the, computational complexity of the proposed approach is proportional to the number of refined coefficients. Therefore, using the proposed approach in image coding applications, we can update the image after receiving each new coefficient and create a continuously refined perception. This can be done without any extra computational cost compared to the normal case where, the image is reconstructed after receiving a predefined number of bits.- A copy of this report has been published in the proceedings of
The 1st Annual Advanced Telecommunications/Information Distribution Research Program Conference, January 21-22, 1997.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 Entropy-Constrained Trellis Coded Quantization: Implementation and Adaptation(1993) Lee, Cheng-Chieh; Farvardin, Nariman; ISREntropy-constrained trellis coded quantization (ECTCQ) of memoryless sources is known to be an efficient source coding technique in the rate-distortion sense. We develop an ECTCQ scheme that employs a symmetric reproduction codebook. The symmetry of the reproduction codebook, while essentially costs no performance loss, is exploited to reduce the memory requirement in entropy coding the ECTCQ output. In practice, a buffer of finite, and preferably small, size is needed to interface the variable-length codewords to the fixed-rate channel. An adaptive ECTCQ (A-ECTCQ) scheme, which uses a buffer-state feedback to control the quantizer characteristics to avoid buffer overflow/underflow, is studied in this work. The choice of encoding delay is an important issue in A- ECTCQ, as too long a delay will adversely impact the performance of the feedback control. We propose a pathwise-adaptive ECTCQ (PA-ECTCQ) that solves the encoding delay problem. Simulation results indicate that, while the buffer overflow/underflow problems of the PA- ECTCQ can be practically eliminated, the overall quantization distortion is increased only negligibly over theoretical performance predictions. Our experiments also suggests that PA- ECTCQ is robust with respect to source mismatch.Item Quantization Over Discrete Noisy Channels Using Rate-One Convolutional Codes(1993) Phamdo, N.; Farvardin, Nariman; ISRWe consider high-rate scalar quantization of a memoryless source for transmission over a binary symmetric channel. It is assumed that, due to its suboptimality, the quantizer's output is redundant. Our aim is to make use of this redundancy to combat channel noise. A rate-one convolutional code is introduced to convert this natural redundancy into a usable form. at the receiver, a maximum a posteriori decoder is employed. An upper bound on the average distortion of the proposed system is derived. An approximation of this bound is computable and we search for that convolutional code which minimizes the approximate upper bound. simulation results for a generalized Gaussian source with parameter a = 0.5 at rate 4 bits/sample and channel crossover provability 0.005 show improvement of 11.9 dB in signal-to-noise ratio over the Lloyd-Max quantizer and 4.6 dB over Farvardin and Vaishampayan's channel-optimized scalar quantizer.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.