Joint Source-channel Coding: Development of Methods and Utilization in Image Communications

In a traditional communication system, the coding process is divided into source coding and channel coding. Source coding is the process of compressing the source signal, and channel coding is the process of error protection. It can be shown that with no delay or complexity constraints and with exact knowledge of the source and channel properties, optimal performance can be obtained with separate source and channel coding. However, joint source-channel coding can lead to performance gains under complexity or delay constraints and offer robustness against unknown system parameters. Multiple description coding is a system for generating two (or more) descriptions of a source, where decoding is possible from either description, but decoding of higher quality is possible if both descriptions are available. This system has been proposed as a means for joint source-channel coding. In this dissertation, the multiple description coding is used to protect against loss of data in an error correcting code caused by a number of channel errors exceeding the correcting ability of the channel code. This is tried on three channel models: a packet erasure channel, a binary symmetric channel, and a block fading channel, and the results obtained with multiple description coding is compared against traditional single description coding. The results show that if a long-term average mean square error distortion measure is used, multiple description coding is not as good as single description coding, except when the delay or block error rate of the channel code is heavily constrained. A direct source-channel mapping is a mapping from amplitude continuous source symbols to amplitude continuous channel symbols, often involving a dimension change. A hybrid scalar quantizer-linear coder (HSQLC) is a direct source-channel mapping where the memoryless source signal is quantized using a scalar quantizer. The quantized value is transmitted on an analog channel using one symbol which can take as many levels as the quantizer, and the quantization error is transmitted on the same channel by means of a simple linear coder. Thus, there is a bandwidth expansion, two channel symbols are produced per source symbol. The channel is assumed to have additive white Gaussian noise and a power constraint. The quantizer levels and the distribution of power between the two symbols are optimized for different source distributions. A uniform quantizer with an appropriate step size gives a performance close to the optimized quantizer both for a Gaussian, a Laplacian, and a uniform memoryless source. The coder performs well compared to other joint source-channel coders, and it is relatively robust against variations in the channel noise level. A previous image coder using direct source-channel mappings is improved. This coder is a subband coder where a classification following the decorrelating filter bank assigns mappings of different rates to different subband samples according to their importance. Improvements are made to practically all the parts of the coder, but the most important one is that the mappings are changed, and particularly, the bandwidth expanding HSQLC is introduced. The coder shows large improvements compared to the previous version, especially at channel qualities near the design quality. For poor channels or high rates, the HSQLC provides a large portion of the improvement. The coder is compared against a combination of a JPEG 2000 coder and a good channel code, and the performance is competitive with the reference, while the robustness against an unknown channel quality is largely improved. This kind of robustness is very important in broadcasting and mobile communications