Source-channel rate allocation for progressive transmission of images

Abstract

Abstract-Progressive image transmission is difficult in the presence of a noisy channel, mainly due to the propagation of errors during the decoding of a progressive bitstream. Excellent results for this problem are made possible through combined source-channel coding, a method that matches the channel code to the source operational rate distortion as well as channel conditions. This paper focuses on the key component of combined source-channel coding: rate allocation. We develop a parametric methodology for rate allocation in progressive source-channel coding. The key to this technique is an empirical model of decoded bit-error rate as a function of the channel code rate. We investigate several scenarios. In the case of the memoryless channel, we present closed-form expressions. For the fading channel and channels with feedback, where closed-form results are elusive, our analysis leads to low-complexity algorithms. The results presented in this paper are applicable to any progressive source code, and any family of channel codes.

Optimization for image transmission over varying channel with MCMC

“…break out loop and the next state x(t + 1) = j is accepted (8) else modify the neighbor space and repeat loop (9) } P[ x(t) = i] is the probability function. x(t) = i represents that at time t, the state of system is i.…”

A linear-complexity distortion optimal scheme for the transmission of packetized progressive bitstreams

“…Let and . By replacing and in (11) and reindexing the variables , , to start from 1, we will arrive at (12) By comparing (9) and (12), we note that except for the variable naming, and represent the same cost function with the same set of constraints. The two optimization problems have the same solution, resulting in (13)…”

“…While the former problem is commonly referred to as a distortion-optimal problem, the latter is known as a rate-optimal problem. Concatenated coding [2], [6], dynamic programming [2]- [4], exhaustive search [6], and gradient-based optimization [1], [10], [9]are among the techniques used to solve different variants of these optimization problems. In [10], we considered distortion-optimal transmission of progressive images over channels with bit errors and packet erasures.…”

A new trellis representation for source-channel rate allocation

“…In [6], they proposed a sub-optimal distortion-based algorithm called the local search algorithm. Other sub-optimal approaches that were developed in [7,8] use Viterbi-like algorithm on tree-like description structures. In addition, other extensions were considered to parallel channels in [9], space-time coded OFDM based MIMO in [10] and Hybrid Automatic Repeat reQuest (HARQ) in [11].…”