A Multiple-Description Trellis Quantizer for Sources With Memory.
Pradeepa Yahampath · 2006
An approach to multiple description block quantiza- tion of a correlated source for transmission over a lossy communi- cation channel is presented. The main idea is to optimally control the redundancy due to both the quantizer index assignment and the residual temporal correlation within a block of quantizer outputs. This is achieved by using a trellis encoder in which each state has an associated index assignment, a finite-state central decoder, and a set of Markov-model based side-decoders which exploit the encoder residual redundancy to reconstruct the source based on an incomplete set of descriptions. An iterative descent algorithm is presented for optimizing the proposed coding system for source statistics and channel loss probability. Simulation results are given which demonstrate the performance of trellis quantizers designed for a Gauss-Markov source. I. INTRODUCTION Multiple description (MD) quantization (1), (2) is a joint source-channel coding method which can achieve reliable communication, by exploiting the diversity available in a com- munication system with multiple transmission paths between the sender and the receiver. A typical example for such a system is a packet-based communication network where some of the transmitted packets do not arrive at the receiver due to network congestion. An M-channel MD quantizer assigns M codewords, referred to as descriptions for every observation from the source (a sample or a vector), which are assumed to be transmitted over independent lossy (erasure) channels. The decoder reconstructs the source based on a subset of m ≤ M descriptions it receives for each source sample, with a fidelity that increases with the number of descriptions m used for reconstruction.