We design a K-description scalar quantizer, whose construction is based on a structure of translated scalar lattices and a lattice in K-1 dimensional space. The use of translated lattices provides a performance advant...
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We design a K-description scalar quantizer, whose construction is based on a structure of translated scalar lattices and a lattice in K-1 dimensional space. The use of translated lattices provides a performance advantage by exploiting a so-called staggering gain. The use of the K-1 dimensional lattice facilitates analytic insight into the performance and significantly speeds up the computation of the index assignment compared to state-of-the-art methods. Using a common decoding method, the proposed index assignment is proven to be optimal for the K-description case. It is shown that the optimal index assignment is not unique. This is illustrated for the two-description case, where a periodic index assignment is selected from possible optimal assignments and described in detail. The performance of the proposed quantizer accurately matches theoretic analysis over the full range of operational redundancies. Moreover, the quantizer outperforms the state-of-the-art MD scheme as the redundancy among the description increases.
In this letter we consider enhancing coding performance of two-stage multipledescription scalar quantization. An enhancement scheme is proposed to make the product of central and side distortions closer to the rate-d...
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In this letter we consider enhancing coding performance of two-stage multipledescription scalar quantization. An enhancement scheme is proposed to make the product of central and side distortions closer to the rate-distortion bound of multipledescription coding under the high-resolution assumption. We show analytically that the second stage refinement information, i.e., the quantized residual errors, can be used to further reduce the side distortions apart from the central distortion, which is substantiated with a memoryless Gaussian source.
An algorithm is presented for online prediction that allows to track the best expert efficiently even when the number of experts is exponentially large, provided that the set of experts has a certain additive structur...
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An algorithm is presented for online prediction that allows to track the best expert efficiently even when the number of experts is exponentially large, provided that the set of experts has a certain additive structure. As an example, we work out the case where each expert is represented by a path in a directed graph and the loss of each expert is the sum of the weights over the edges in the path. These results are then used to construct universal limited-delay schemes for lossy coding of individual sequences. In particular, we consider the problem of tracking the best scalar quantizer that is adaptively matched to the source sequence with piecewlse different behavior. A randomized algorithm is presented which can perform, on any source sequence, asymptotically as well as the best scalar quantization algorithm that is matched to the sequence and is allowed to change the employed quantizer for a given number of times. The complexity of the algorithm is quadratic in the sequence length, but at the price of some deterioration in performance, the complexity can be made linear. Analogous results are obtained for sequential multiresolution and multipledescription scalar quantization of individual sequences.
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