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Optimal two-description scalar quantizer design

机译:最佳二描述标量量化器设计

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Multiple description quantization is a signal compression technique for robust networked multimedia communication. In this paper we consider the problem of optimally quantizing a random variable into two descriptions, with each description being produced by a side quantizer of convex codecells. The optimization objective is to minimize the expected distortion given the probabilities of receiving either and both descriptions. The problem is formulated as one of shortest path in a weighted directed acyclic graph with constraints on the number and types of edges. An O(K1K2N3) time algorithm for designing the optimal two-description quantizer is presented, where N is the cardinality of the source alphabet, and K-1, K-2 are the number of codewords of the two quantizers, respectively. This complexity is reduced to O(K1K2N2) by exploiting the Monge property of the objective function. Furthermore, if K-1 = K-2 = K and the two descriptions are transmitted through two channels of the same statistics, then the optimal two-description quantizer design problem can be solved in O(KN2) time.
机译:多描述量化是用于鲁棒网络多媒体通信的信号压缩技术。在本文中,我们考虑将随机变量最佳化为两个描述的问题,每个描述都是由凸码元的侧面量化器产生的。考虑到接收到任一描述和两个描述的可能性,优化目标是使预期的失真最小化。该问题被公式化为加权有向无环图中最短路径之一,该图对边的数量和类型有约束。提出了一种用于设计最佳二描述量化器的O(K1K2N3)时间算法,其中N是源字母表的基数,K-1,K-2分别是两个量化器的代码字数。通过利用目标函数的Monge属性,该复杂度降低为O(K1K2N2)。此外,如果K-1 = K-2 = K并且两个描述是通过两个具有相同统计量的通道传输的,则可以在O(KN2)时间内解决最优的两个描述量化器设计问题。

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