@conference {
	title = {Motion-Compensated Wavelet Transform Coding for Full-Motion Color Video Signals},
	author = {Zhang, Ya-Qin. , Zafar, Sohail., Baras, John S.},
	year = {1991},
	month = {11/1991},
	abstract = {The discrete wavelet transform (DWT) has recently received much attention in the context of image~processing due to its flexibility in representing nonstationary image signals and its ability in ~adapting human visual characteristics. Its relationships to subband coding, Gabor transform, pyramid~structure and other intermediate spatial-frequency representation have been investigated [MALL89][BURT 89][VETT90]. Most of current research in DWT has been concentrated on still imagecoding in the area of data compression. In [BAAZ90], DWT was applied to decompose a sequence~of images and no coding results were reported. It is the purpose of this paper to apply DWT to~compress full motion color TV video signals using inter-frame motion-compensated coding, which,~to the best of our knowledge, has not been researched previously.

The compression scheme we implemented in our work is an interframe hybrid DPCM/DWT scheme~with a new motion compensation method. After motion compensation, the displaced frame difference(DFD) residue video signal is decomposed by wavelet representation and the wavelet pyramid~samples are uniformly quantized in each band. The quantization process takes advantage of human~visual characteristics and coefficients are weighted prior to the quantization. Motion vectors are~noiselessly coded. All quantities are entropy-coded adaptively prior to the transmission. The number~of samples in the wavelet representation is equal to the samples in the original frame as a consequence~of orthogonality of the decomposition.~

Two new block-based motion estimation schemes, namely: intra-frame and inter-frame predictive~motion estimation schemes, are used to find the displacement information prior to the wavelet~decomposition on the displace difference frame. In the proposed intra-frame predictive pattern~searching scheme (PPS), motion information in the neighboring blocks is exploited. This is especially~useful in estimating motion vectors of video signals which contain large homogeneous areas and~translations caused by object moving or camera panning. Therefore, motion vectors for these areas~are highly correlated. The purpose of intra-frame PPS is to take advantage of this correlation and~ hereby reduce the searching area. The proposed inter-frame predictive pattern searching scheme is based on the "inertia" property of moving object. The motion vectors of a block is estimated from~those of previous frames represented by an autoregressive model. The propose intra/inter PPS~Schemes not only significantly reduce the computational complexity of motion estimations, it also~gives more realistic motion vectors.

The proposed motion compensated DWT coding scheme is implemented in our Abekas-60 based~video compression testbed. The Abekas A-60 is basically a digital video recorder which allows a~real-time playback of 25 second recorded CCIR 601 digital video. The Abekas system is interfaced~with a SPARC workstation, where the compression software resides. By software simulation of the~compression/decompression techniques in the SPARC, we can reconstruct video segments to~compare with the original signal via a real-time play back. Therefore, we can evaluate compression performance, quality degradation, computational efficiency of different coding algorithms. The test~sequences "CAR" and "TOPGUN" we use are two full-motion color video sequences in CCIR 601~format with 720 by 480 per frame, 16 bits per pixel in YUV format (or 24 bits in RGB format) and~30 frames/60 fields per second. We measured the first-order entropy of the DFD signal, the entropy~of motion vectors, the reconstructed signal-to-noise ratio at a coding rate ranging from 1Mbps to 3~Mbps. Those results are also compared with the MPEG-like scheme which uses a motion~compensated hybrid DPCM/DCT scheme and is currently being standardized by the CCITT MPEG~committee. The subjective and comparative results suggest that the DWT can be successfully applied~to full-motion video compression and less "blocking effect" is observed compared to the DCT-based~block transform coding with about the same data rate and signal-to-noise ratio.},
	address = {Boston, Massachusetts},
	booktitle = {SPIE-Visual Communications and Image Processing {\textquoteright}91: Sixth in a Series},
	pages = {pp. 301-316}
}
@conference {
	title = {Consistent Estimation of the Order of Hidden Markov Chains},
	author = {Baras, John S., Finesso, Lorenzo.},
	year = {1991},
	month = {09/1991},
	abstract = {The structural parameters of many statistical models can be estimated maximizing a penalized version of the likelihood function. We use this idea to construct strongly consistent estimators of the order of Hidden Markov Chain models. The specification of the penalty term requires precise information on the rate of growth of the maximized likelihood ratio. We find an upper bound to the rate using results from Information Theory. We give sufficient conditions on the penalty term to avoid overestination and underestimation of the order. Examples of penalty terms that~generate strongly consistent estimators are also given.},
	address = {Lawrence, Kansas},
	booktitle = {Stochastic Theory and Adaptive Control Workshop},
	pages = {pp. 26-39},
	organization = {University of Kansas}
}
@conference {
	title = {Two-Dimensional Signal Deconvolution: Design Issues Related to a Novel Multisensor-based Approach},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1991},
	month = {07/1991},
	abstract = {We employ recent results of analysis in several complex variables to come up with a set of compactly supported approximate deconvolution kernels for the reconstruction of a two dimensional signal based on multiple linearly degraded versions of the signal with a family of kernels that satisfy suitable technical conditions. We discuss the question of convergence of the proposed deconvolution kernels, present simulation results that demonstrate the gain in bandwidth, and propose two data parallel grid layouts for the off-line computation of the deconvolution kernels.},
	address = {San Diego, California},
	booktitle = {SPIE 1991 International Symposium on Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision},
	pages = {pp. 356-366}
}
@conference {
	title = {A Bayesian Matching Technique for Detecting Simple Objects in Heavily Noisy Environment},
	author = {Baras, John S., Frantzeskakis, Emmanuel.},
	year = {1991},
	month = {07/1991},
	abstract = {The template matching problem for binary corrupted images with spatially white, binary, symmetric noise is studies. We compare matching based directly on the pixel-value image data as well as data coded by two simple schemes: a modification of the Hadamard~basis and direct coarsening of resolution. Bayesian matching rules based on M-ary~hypothesis are developed. The performance evaluation of these rules is provided,

The paper presents a study of the trade-off between the quantization level and the ability of detecting an object in an image. This trade-off depends on the (external) noise generated at the moment we receive the uncoded image.

The sum-of pixels and the histogram statistics are introduced in order to reduce the computational load inherent in the correlation statistic, with the resulting penalty of a higher probability~of false alarm rate.

In the present work~we demonstrate by examples that it is beneficial for recognition to combine an image coding technique with an algorithm extracting some "basic" information from the image. In other words~coding (for compression) helps recognition. Numerical results illustrate this claim.},
	address = {San Diego, California},
	booktitle = {SPIE 1991 International Symposium on Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision},
	pages = {pp. 341-353}
}
@conference {
	title = {Convergence of Kohonen{\textquoteright}s Learning Vector Quantization},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1991},
	month = {06/1991},
	abstract = {Kohonen{\textquoteright}s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing then to k; templates called Voronoi vectors. The locations of these vectors are determined from past labeled data through a learning algorithm. When learning is complete, the class of a new observation is the same as the class of the closest Voronoi vector. Hence LVQ is similar to nearest neighbors, except that instead of all of the past observations being searched only the k Voronoi vectors are searched.

In this paper, we show that the LVQ~learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the learning algorithm which results in more robust convergence.},
	address = {San Diego, California},
	booktitle = {The 1990 International Joint Conference on Neural Networks (IJCNN)},
	pages = { pp. 17-20}
}
@conference {
	title = {Real Time Architectures for the Zakai Equation and Applications},
	author = {Baras, John S.},
	year = {1991},
	month = {06/1991},
	abstract = {We examine in detail real-time architectures for the sequential detection and/or estimation problems for diffusion type signals. We demonstrate the fundamental role played by Zaki equation in defining candidate architectures. For scalar and two dimensional state models an architecture based on systolic arrays is derived. For higher dimensional implementation~of the Multigrid algorithm is derived. Properties of the architectures and practical hardware implementation results are also reported.},
	booktitle = {Conference honoring Professor Moshe Zakai on his 60th Birthday},
	pages = {pp. 15-38}
}
@conference {
	title = {Predictive Block-Matching Motion Estimation Schemes for Video Compression -- Part I:  Inter-Block Prediction},
	author = {Zafar, Sohail., Zhang, Ya-Qin. , Baras, John S.},
	year = {1991},
	month = {04/1991},
	abstract = {A new motion estimation/compensation~scheme for TV coding is proposed in this paper. This~scheme is based on prediction of inter-block motion information using minimum absolute difference block matching and hence substantially increases the searching and computation efficiency. A comparison is also made with the "optimal" full motion searching scheme for a standard test sequence.},
	address = {Williamsburg, Virginia},
	booktitle = {The IEEE Southeastcon {\textquoteright}91 Conference},
	pages = {pp. 1088-1092}
}
@conference {
	title = {Predictive Block-Matching Motion Estimation Schemes for Video Compression -- Part II: Inter-frame Prediction of Motion Vectors},
	author = {Zhang, Ya-Qin. , Zafar, Sohail., Baras, John S.},
	year = {1991},
	month = {04/1991},
	abstract = {A new predictive block-matching motion estimation scheme is proposed in this paper for efficient video codec design. The proposed Scheme is based on the "inertia" effect of natural video scenes and takes advantage of the motion vectors obtained in the previous frames. Not only motion searching time and overhead are significantly reduced, the proposed scheme also provides realistic motion tendency, which is very useful for frame skipping/interpolation techniques. These advantages are also demonstrated by some experimental results obtained on Abekas video system.},
	address = {Williamsburg, Virginia},
	booktitle = {The IEEE Southeastcon {\textquoteright}91 Conference},
	pages = {pp. 1093-1095}
}
@conference {
	title = {Two-Dimensional Signal Deconvolution Using Multiple Sensors},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1991},
	month = {03/1991},
	abstract = {We consider the two dimensional Analytic Bezout Equation (ABE) and investigate the properties of a particular solution, based on certain conditions imposed on the convolution kernels. We propose the use of approximate deconvolution kernels, prove compactness of their support, and discuss various design problems pertaining to the question of convergence of the proposed deconvolution kernels. We point out the fact that these approximate deconvolution kernels exhibit a strong asymmetry in the FT domain, and propose solutions to this problem. Simulation results are presented in order to demonstrate the gain in bandwidth, the asymmetry of the FT of the overall system, and the effectiveness of the proposed solutions to compensate for this asymmetry.},
	address = {Baltimore, MD},
	booktitle = {The 1991 Conference on Information Sciences and Systems},
	pages = {pp. 594-599},
	organization = {Johns Hopkins University}
}
