This paper presents an admission control framework to support 'streaming' of audio-video data over packet-switched networks. To ensure smooth functioning, the network provides a degree of assurance to the applications in the form of soft QOS guarantees. A measurement based admission control algorithm is developed to enable the network to provide the required service assurances. Sources are characterized using a statistical bounding model, which is fairly universal. A measurement procedure is first developed for online estimation of model parameters. This measurement procedure is then used in the admission control framework. Simulation results are presented and the performance of the algorithms are evaluated.

We present a practical video coding algorithm for use at very low bit rates. For efficient coding at very low bit rates, it is important to intelligently allocate bits within a frame, and so a powerful variable-rate algorithm is required. We use vector quantization to encode the motion-compensated residue signal in an H.263-like framework. For a given complexity, it is well understood that structured vector quantizers perform better than unstructured and unconstrained vector quantizers. A combination of structured vector quantizers is used in our work to encode the video sequences. The proposed codec is a multistage residual vector quantizer, with transform vector quantizers in the initial stages. The transform-VQ captures the low-frequency information, using only a small portion of the bit budget, while the later stage residual VQ captures the high-frequency information, using the remaining bits. We used a strategy to adaptively refine only areas of high activity, using recursive decomposition and selective refinement in the later stages. An entropy constraint was used to modify the codebooks to allow better entropy coding of the indexes. We evaluate the performance of the proposed codec, and compare this data with the performance of the H.263-based codec. Experimental results show that the proposed codec delivered significantly better perceptual quality along with better quantitative performance.

Discusses a method of compression of 3-D MRI images. A subband coding scheme with an octave splitting is proposed. The higher frequency subbands are encoded using an octtree structure. Octtrees provide a convenient data structure to store the sparse information present in the higher frequency subbands. Each unit block of the octtree is coded using a hybrid vector and scalar quantization scheme. The baseband is encoded using a DPCM based method using a nonlinear neural network predictor. This scheme provides an elegant way of compression of MRI images with minimum error.

In this paper we address the task of segmentation of objects from photographs. A method of extraction of features based on the zero-crossings of a wavelet transform is described. The wavelet transform basis functions are derived from the second derivative of a Gaussian function. The extracted features are then used in a multilevel hypothesis generate and test algorithm to locate the objects of interest. The matching algorithm is based on the springs and templates framework of Fischler and Eschlanger (1973). The zero-crossings of the wavelet coefficients at different scales are combined in the model-matching stage to generate possible candidates. We apply this method to segment human faces from newspaper photographs.

Residual vector quantizers have been proposed to overcome the search complexity of regular single stage vector quantizers. We present a design algorithm for residual vector quantizer codebooks. An attempt is made to make full use of the sequential search nature of the encoding process. An error energy is formulated based on the distortion criterion with a constraint imposed to optimize for the sequential search. The codebook design is formulated as multidimensional minimization problem, where the error energy is minimized to obtain the required codebooks. The proposed algorithm is based on the gradient descent algorithm.>