On common information and the encoding of sources that are not successively refinable

Kumar Viswanatha, E. Akyol, Tejaswi Nanjundaswamy, K. Rose
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引用次数: 6

Abstract

This paper focuses on a new framework for scalable coding of information based on principles derived from common information of two dependent random variables. In the conventional successive refinement setting, the encoder generates two layers of information called the base layer and the enhancement layer. The first decoder, which receives only the base layer, produces a coarse reconstruction of the source, whereas the second decoder, which receives both the layers, uses the enhancement layer to refine the information further leading to a finer reconstruction. It is popularly known that asymptotic rate-distortion optimality at both the decoders is possible if and only if the source-distortion pair is successively refinable. However when the source is not successively refinable under the given distortion metric, it is impossible to achieve rate-distortion optimality at both the layers simultaneously. For this reason, most practical system designers resort to storing two individual representations of the source leading to significant overhead in transmission/storage costs. Inspired by the breadth of applications, in this paper, we propose a new framework for scalable coding wherein a subset of the bits sent to the first decoder is not sent to the second decoder. That is, the encoder generates one common bit stream which is routed to both the decoders, but unlike the conventional successive refinement setting, both the decoders receive an additional individual bitstream. By relating the proposed framework with the problem of common information of two dependent random variables, we derive a single letter characterization for the minimum sum rate achievable for the proposed setting when the two decoders are constrained to receive information at their respective rate-distortion functions. We show using a simple example that the proposed framework provides a strictly better asymptotic sum rate as opposed to the conventional scalable coding setup when the source-distortion pair is not successively refinable.
关于公共信息和不能连续细化的源的编码
基于两个相关随机变量的公共信息导出的原理,提出了一种新的信息可扩展编码框架。在传统的连续细化设置中,编码器产生两层信息,称为基础层和增强层。仅接收基础层的第一解码器产生源的粗重建,而接收两层的第二解码器使用增强层进一步细化信息导致更精细的重建。众所周知,当且仅当源-失真对连续可细化时,两个解码器的渐近率失真最优性是可能的。然而,当在给定的失真度量下源不能连续细化时,不可能在两层同时实现率失真最优性。出于这个原因,大多数实际的系统设计人员采用存储源的两种单独表示,导致传输/存储成本的显著开销。受到广泛应用的启发,在本文中,我们提出了一种新的可扩展编码框架,其中发送到第一个解码器的比特子集不会发送到第二个解码器。也就是说,编码器生成一个公共比特流,该比特流被路由到两个解码器,但与传统的连续细化设置不同,两个解码器都接收一个额外的单个比特流。通过将所提出的框架与两个相关随机变量的公共信息问题联系起来,我们推导出了当两个解码器被约束以各自的速率失真函数接收信息时,所提出的设置可实现的最小和速率的单个字母表征。我们使用一个简单的例子表明,当源-失真对不能连续细化时,与传统的可扩展编码设置相比,所提出的框架提供了严格更好的渐近和速率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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