Lossless and near-lossless source coding for multiple access networks

A multiple access source code (MASC) is a source code designed for the following network configuration: a pair of correlated information sequences {X-i}(i=1)(infinity), and {Y-i}(i=1)(infinity) is drawn independent and identically distributed (i.i.d.) according to joint probability mass function (p.m.f.) p(x, y); the encoder for each source operates without knowledge of the other source; the decoder jointly decodes the encoded bit streams from both sources. The work of Slepian and Wolf describes all rates achievable by MASCs of infinite coding dimension (n --> infinity) and asymptotically negligible error probabilities (P-e((n)) --> 0). In this paper, we consider the properties of optimal instantaneous MASCs with finite coding dimension (n 0) performance. The interest in near-lossless codes is inspired by the discontinuity in the limiting rate region at P-e((n)) = 0 and the resulting performance benefits achievable by using near-lossless MASCs as entropy codes within lossy MASCs. Our central results include generalizations of Huffman and arithmetic codes to the MASC framework for arbitrary p(x, y), n, and P-e((n)) and polynomial-time design algorithms that approximate these optimal solutions

Lossless source coding for multiple access networks

A multiple access source code (MASC) is a source code designed for the following network configuration: a pair of jointly distributed information sequences {Xi}i=1∞ and {Yi}i=1∞ is drawn i.i.d. according to joint probability mass function (p.m.f.) p(x,y); the encoder for each source operates without knowledge of the other source; the decoder receives the encoded bit streams of both sources. The rate region for MASCs with arbitrarily small but non-zero error probabilities was studied by Slepian and Wolf. In this paper, we consider the properties of optimal truly lossless MASCs and apply our findings to practical truly lossless and near lossless code design

Broadcast system source codes: a new paradigm for data compression

Broadcast systems play a central role in an enormous variety of network technologies in which one system node must simultaneously send either the same or different information to multiple nodes in the network. Systems incorporating broadcast components include such diverse technologies as wireless communications systems, web servers, distributed computing devices, and video conferencing systems. Currently, the compression algorithms (or source codes) employed in these devices fail to take advantage of the characteristics specific to broadcast systems. Instead, they treat a single node transmitting information to a collection of receivers as a collection of single-transmitter single-receiver communications problems and employ an independent source code on each. This approach is convenient, since it allows direct application of traditional compression techniques in a wide variety of broadcast system applications. Nonetheless, we here argue that the approach is inherently flawed. Our innovation in this paper is to treat the general broadcast system (with an arbitrary number of receivers and both specific and common information) as an inseparable whole and consider the resulting source coding ramifications. The result is a new paradigm for data compression on general broadcast systems. In this work, we describe this broadcast system source coding paradigm and examine the potential gains achievable by moving away from more conventional methods

Uniquely decodable multiple access source codes

The Slepian-Wolf bound raises interest in lossless code design for multiple access networks. Previous work treats instantaneous codes. We generalize the Sardinas and Patterson test and bound the achievable rate region for uniquely decodable codes. The Kraft inequality is generalised to produce the necessary conditions on the codeword lengths for uniquely decodable-side information source code

Optimal code design for lossless and near lossless source coding in multiple access networks

A multiple access source code (MASC) is a source code designed for the following network configuration: a pair of correlated information sequences {Xi}i=1∞ and {Yi }i=1∞ is drawn i.i.d. according to the joint probability mass function (p.m.f.) p(x,y); the encoder for each source operates without knowledge of the other source; the decoder jointly decodes the encoded bit streams from both sources. The work of Slepian and Wolf (1973) describes all rates achievable by MASCs with arbitrarily small but non-zero error probabilities but does not address truly lossless coding or code design. We consider practical code design for lossless and near lossless MASCs. We generalize the Huffman and arithmetic code design algorithms to attain the corresponding optimal MASC codes for arbitrary p.m.f. p(x,y). Experimental results comparing the optimal achievable rate region to the Slepian-Wolf region are included

Multiresolution source coding using entropy constrained dithered scalar quantization

In this paper, we build multiresolution source codes using entropy constrained dithered scalar quantizers. We demonstrate that for n-dimensional random vectors, dithering followed by uniform scalar quantization and then by entropy coding achieves performance close to the n-dimensional optimum for a multiresolution source code. Based on this result, we propose a practical code design algorithm and compare its performance with that of the set partitioning in hierarchical trees (SPIHT) algorithm on natural images

Network vector quantization

We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples

Side information source coding: low complexity design and source independence

Correlated sources X and Y are drawn i.i.d. according to probability mass function (pmf) p(x,y). In the side information source code (SISC) configuration: p(x,y) is known a priori to both the encoder and the decoder; the encoder knows X but not Y; the decoder knows Y but not X; the encoder encodes X and transmits the description of X to the decoder; the decoder reconstructs X using the source description and side information Y. The universal linked side information source code (ULSISC) configuration modifies the SISC configuration by assuming that p(x,y) is unknown a priori and that a asymptotically negligible amount of communication is allowed from the decoder to the encoder. We combine SISC design with ULSISC theory to build the codes for applications where the source statistics are unknown at design time. Experimental results compare ULSISC and SISC performance

Low complexity code design for lossless and near-lossless side information source codes

The instantaneous side of information source code (SISC) design is considered. In the SISC configuration, the encoder describes source X to the decoder; the decoder uses this description and side information Y, to reconstruct X. Prior work on lossless and near-lossless SISC design demonstrates that globally optimal design is NP-hard. A family of polynomial complexity code design algorithms is introduced that approximates the optimal solution for lossless and near-lossless SISCs. The algorithm may be used to design both Huffman and arithmetic SISCs for an arbitrary probability mass function p(x,y). Experimental results comparing the resulting performances to each other and to the theoretical limit are included

Pitching Private Medicare Plans: An Analysis of Medicare Advantage and Prescription Drug Plan Advertising

Analyzes television, print, and radio ads for private Medicare plans to assess what types of information insurers emphasize and de-emphasize, what populations they target, and which type of plan they promote in trying to influence beneficiaries' choices