P.A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projection onto convex sets. In Data Compression Conference, Snowbird, Utah, March 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....idea is to decorrelate the vector components and then to introduce again correlation between coefficients, but in a known and controlled manner, so that erased coefficients can be statistically estimated from those received. Techniques based on overcomplete frame expansions have been proposed in [6], 15] 21] Most of the previous work on MD coding focuses on the case of memoryless sources or sources with finite memory. In [18] Ingle et al. consider the problem of designing DPCM systems for MD coding of sources with memory. Batllo et al. considered a similar problem and proposed a ....

P.A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projection onto convex sets. In Data Compression Conference, Snowbird, Utah, March 1999.

....expansions, tight frames, intersection lattice, consistency, periodic quantizers. I. INTRODUCTION AND MOTIVATION Quantized redundant expansions are useful in different applications such as oversampled A D conversion of band limited signals [1, 2, 3, 4, 5] and multiple description quantization [6, 7, 36, 37]. In the first case, the purpose of using redundant expansions is to attain accurate digital signal representations under scenarios where the cost of using high rate quantization is much higher than that of having a high oversampling or redundancy. The most important case is the analog to digital ....

.... domain has been observed experimentally for quantized (two dimensional) steerable transforms [10] so that increasing the number of orientations yields a gain in energy compaction [11] Quantized overcomplete expansions also arise in the context of joint source channel coding for erasure channels [6, 7, 36, 37]. There are two major factors that determine the accuracy that can be attained using quantized overcom plete expansions: the reconstruction algorithm and the quantization scheme. There has been extensive research work aiming at finding reconstruction algorithms that are optimal or near optimal ....

P. Chou, S. Mehrotra and A. Wang, "Multiple description decoding of overcomplete expansions using projections onto convex sets," in Proc. of Data Compression Conf., 1999, pp. 72-81.

.... often modeled through generalized multiple description coding (MDC) in which the encoder produces multiple descriptions of the source data, each exactly filling one packet [2] Recently, the generalized MDC framework has received considerable attention and many new algorithms have been proposed [2, 3, 4, 5]. In this work, we concentrate on networks which do not provide preferential treatment to packets and instead erase them randomly. Usually, however, data vary in importance. High magnitude, low frequency data that let us recognize coarse structures in an image are much more important than low ....

....by Goyal et al. 2] When an appropriate frame expansion is used, the additional trees will contribute useful information and improve the PSNR at low losses. We will use Chou et al. s method for reconstructing a signal from a subset of frame coefficients based on projections onto convex sets [3]. In future research we will optimize the tree packetization, total number of descriptions, and DWT levels. In addition, the basic idea behind MD SPIHT can be extended to other image and video compression schemes. For more information see [10] The authors would like to thank Professor William ....

P. A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projections onto convex sets. In Proceedings Data Compression Conference, March 1999.

....less than N are received. This situation commonly occurs in transmissions over the Internet, where network congestion causes packet loss. The goal is to maximize the quality of the reconstruction given a set of received packets and the descriptions they contain. Other MD related papers include [2, 3, 4, 5, 6, 7]. However, many of these works do not consider the potential of forward error correction (FEC) A notable exception is the work of Puri, Ramchandran, and Kozintsev [8] We use systematic Reed Solomon (RS) codes to generate FEC. These codes are very effective at recovering erased symbols when the ....

P. A. Chou, S. Mehrotra, and A. Wang, "Multiple description decoding of overcomplete expansions using projections onto convex sets," in Proceedings of Data Compression Conference, pp. 72--81, Mar. 1999.

.... often modeled through generalized multiple description coding (MDC) in which the encoder produces multiple descriptions of the source data, each exactly filling one packet [2] Recently, the generalized MDC framework has received considerable attention and many new algorithms have been proposed [2, 3, 4, 5]. In this work, we concentrate on networks which do not provide preferential treatment to packets and instead erase them randomly. Usually, however, data vary in importance. High magnitude, low frequency data that let us recognize coarse structures in an image are much more important than low ....

....by Goyal et al. 2] When an appropriate frame expansion is used, the additional trees will contribute useful information and improve the PSNR at low losses. We will use Chou et al. s method for reconstructing a signal from a subset of frame coefficients based on projections onto convex sets [3]. In future research we will optimize the tree packetization, total number of descriptions, and DWT levels. In addition, the basic idea behind MD SPIHT can be extended to other image and video compression schemes. For more information see [10] The authors would like to thank Professor William ....

P. A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projections onto convex sets. In Proceedings Data Compression Conference, March 1999.

....as the Internet or wireless networks. The goal is to maximize the quality of the reconstruction given the received descriptions. Many different algorithms for MDC have been presented in the literature. Some approaches add redundancy in the form of repeated or correlated data before transmission [15, 5, 9, 3, 6, 8, 4]. In multiple description scalar and vector quantization [21, 22, 19] multiple quantizers refine each other into lower distortion quantizers if more than one description is received. All of these systems yield image quality that depends on which descriptions are received. In this work, we use ....

P. A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projections onto convex sets. In Proceedings of Data Compression Conference, pages 72--81, March 1999.

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P.A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projection onto convex sets. In Data Compression Conference, Snowbird, Utah, March 1999.

No context found.

P.A. Chou, S. Mehrotra, and A. Wang. Multiple description decoding of overcomplete expansions using projection onto convex sets. In Data Compression Conference, Snowbird, Utah, March 1999.

No context found.

P. Chou, S. Mehrotra, and A. Wang, "Multiple description decoding of overcomplete expansions using projections onto convex sets," in Proc. Data Compression Conf., Snowbird, UT, 1999, pp. 72--81.

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P.A. Chou, S. Mehrotra, and A. Wang, "Multiple description decoding of overcomplete expansions using projections onto convex sets," in Proc. IEEE Data Compression Conf., Snowbird, UT, Mar. 1999, pp. 72-81.

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