Institute for Systems Research

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Author: search.filters.author.Baras, John S.Subject: search.filters.subject.data compression

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Now showing 1 - 7 of 7
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    A Practical Transmission System Based on the Human Visual Model for Satellite Channels
    (1999) Gu, Junfeng; Jiang, Yimin; Baras, John S.; Baras, John S.; ISR; CSHCN
    This paper presents a practical architecture for joint source-channel coding of human visual model-based video transmission over a satellite channel. Perceptual distortion model just-noticeable-distortion (JND) is applied to improve the subjective quality of compressed videos. 3-D wavelet decomposition can remove spatial and temporal redundancy and provide the scalability of video quality.

    In order to conceal errors occurring under bad channel conditions, a novel slicing method and a joint source channel coding scenario that combines RCPC with CRC and utilizes the distortion information to allocate convolutional coding rates are proposed. A new performance index based on JND is proposed and used to evaluate the overall performance at different signal-to-noise ratios (SNR). Our system uses OQPSK modulation scheme.

    The research and scientific content in this material has been submitted to Globecom'99.
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    Combined Compression and Classification with Learning Vector Quantization
    (1998) Baras, John S.; Dey, Subhrakanti; ISR
    Combined compression and classification problems are becoming increasinglyimportant in many applications with large amounts of sensory data andlarge sets of classes. These applications range from aided target recognition(ATR), to medicaldiagnosis, to speech recognition, to fault detection and identificationin manufacturing systems. In this paper, we develop and analyze a learningvector quantization-based (LVQ) algorithm for the combined compressionand classification problem. We show convergence of the algorithm usingtechniques from stochastic approximation, namely, the ODE method. Weillustrate the performance of our algorithm with some examples.
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    Accurate Segmentation and Estimation of Parametric Motion Fields for Object-based Video Coding using Mean Field Theory
    (1997) Haridasan, Radhakrishan; Baras, John S.; ISR; CSHCN
    We formulate the problem of decomposing a scene into its constituent objects as one of partitioning the current frame into objects comprising it. The motion parameter is modeled as a nonrandom but unknown quantity and the problem is posed as one of Maximum Likelihood (ML) estimation. The MRF potentials which characterize the underlying segmentation field are defined in a way that the spatio-temporal segmentation is constrained by the static image segmentation of the current frame. To compute the motion parameter vector and the segmentation simultaneously we use the Expectation Maximization (EM) algorithm. The E-step of the EM algorithm, which computes the conditional expectation of the segmentation field, now reflects interdependencies more accurately because of neighborhood interactions. We take recourse to Mean Field theory to compute the expected value of the conditional MRF. Robust M-estimation methods are used in the M- step. To allow for motions of large magnitudes image frames are represented at various scales and the EM procedure is embedded in a hierarchical coarse-to-fine framework. Our formulation results in a highly parallel algorithm that computes robust and accurate segmentations as well as motion vectors for use in low bit rate video coding.

    This report has been submitted as a paper to the SPIE conference on Visual Communications and Image Processing - VCIP98 to be held in San Jose, California on Jan 24- 30, 1998.
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    Existence and Construction of Optimal Wavelet Basis for Signal Representation
    (1994) Zhuang, Y.; Baras, John S.; ISR; CSHCN
    We study the problem of choosing the optimal wavelet basis with compact support for signal representation and provide a general algorithm for computing the optimal wavelet basis. We first briefly review the multiresolution property of wavelet decomposition and the conditions for generating a basis of compactly supported discrete wavelets in terms of properties of quadrature mirror filter (QMF) banks. We then parametrize the mother wavelet and scaling function through a set of real coefficients. We further introduce the concept of decomposition entropy as an information measure to describe the distance between the given signal and its projection onto the subspace spanned by the wavelet basis in which the signal is to be reconstructed. The optimal basis for a given signal is obtained through minimizing this information measure. We have obtained explicitly the sensitivity of dilations and shifts of the mother wavelet with respect to the coefficient set. A systematic approach is developed in this paper to derive the information gradient with respect to the parameter set from a given square integrable signal and a discrete basis of wavelets. The existence of the optimal basis for the wavelets has been proven in this paper. a gradient based optimization algorithm is developed for computing the optimal wavelet basis.
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    Optimal Wavelet Basis Selection for Signal Representation
    (1994) Zhuang, Y.; Baras, John S.; ISR; CSHCN
    We study the problem of choosing the optimal wavelet basis with compact support for signal representation and provide a general algorithm for computing the optimal wavelet basis. We first briefly review the multiresolution property of wavelet decomposition and the conditions for generating a basis of compactly supported discrete wavelets in terms of properties of quadrature mirror filter (QMF) banks. We then parametrize the mother wavelet and scaling function through a set of real coefficients. We further introduce the concept of information measure as a distance measure between the signal and its projection onto the subspace spanned by the wavelet basis in which the signal is to be reconstructed. The optimal basis for a given signal is obtained through minimizing this information measure. We have obtained explicitly the sensitivity of dilations and shifts of the mother wavelet with respect to the coefficient set. A systematic approach is developed here to derive the information gradient with respect to the parameter set for a given square integrable signal and the optimal wavelet basis. A gradient based optimazation algorithm is developed in this paper for computing the optimal wavelet basis.
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    Time-Recursive Computation, Part II: Methodology, and Application on QMF Banks and ELT
    (1993) Frantzeskakis, Emmanuel N.; Baras, John S.; Liu, K.J. Ray; ISR
    Recent advances in ISDN have promoted applications such as video- phone, tele-conferencing and HDTV, that demand real-time processing of large volume audio, video and speech data. Being the only refuge for this intense computation, the VLSI technology favors modular and regular designs with local communication requirements. In this light, the framework for time-recursive computation, presented in part I [7] of this two-part paper, provides the background for designing efficient VLSI implementations, capable of accommodating high throughput requirements. In part II, we develop a routine that can be used for designing the time-recursive architecture of a given linear operator in a systematic manner. Three classes of QMF banks are used as design examples: the lossless QMF bank, the cosine modulated QMF bank and two Extended Lapped Transforms, one of them being the Modulated Lapped Transform (MLT). In addition to demonstrating the use of the design procedure, these examples provide novel results, interesting on their own right. In particular, the time-recursive architecture we propose for an N - point MLT, also known as Modified DCT or Time Domain Aliasing Cancellation (TDAC) transform, requires 2N + 3 multipliers, 3N + 3 adders and N - 1 rotation circuits.
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    Time-Recursive Computation and Real-Time Parallel Architectures, Part I: Framework
    (1993) Frantzeskakis, Emmanuel N.; Baras, John S.; Liu, K.J. Ray; ISR
    The time-recursive computation has been proved as a particularly useful tool in real-time data compression, in transform domain adaptive filtering and in spectrum analysis. Unlike the FFT based ones, the time-recursive architectures require only local communication. Also, they are modular and regular, thus they are very appropriate for VLSI implementation and they allow high degree of parallelism. In this two part paper, we establish an architectural frame work for parallel time-recursive computation. In part I, we consider a class of linear operators that consists of the discrete time, time invariant, compactly supported, but otherwise arbitrary kernel functions. We show that the structure of the realization of a given linear operator is dictated by the decomposition of the latter with respect to proper basis functions. An optimal way for carrying out this decomposition is demonstrated. The parametric forms of the basis functions are identified and their properties pertinent to the architecture design are studied. A library of architectural building modules capable of realizing these functions is developed. An analysis of the implementation complexity for the aforementioned modules is conducted. Based on this framework, an architecture design procedure is developed in Part II [12] that can be used for routinely obtaining the time-recursive architecture of a given linear operator.