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Abstract
Distributed source coding, the separate encoding and joint decoding of statistically dependent sources, has many potential applications ranging from lower complexity capsule endoscopy to higher throughput satellite imaging. This dissertation improves distributed source coding algorithms and the analysis of their coding performance to handle uncertainty in the statistical dependence among sources.
We construct sequences of rate-adaptive low-density parity-check (LDPC) codes that enable encoders to switch flexibly among coding rates in order to adapt to arbitrary degrees of statistical dependence. These code sequences operate close to the Slepian-Wolf bound at all rates. Rate-adaptive LDPC codes with well-designed source degree distributions outperform commonly used rate-adaptive turbo codes.
We then consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive performance bounds for binary and multilevel models of this problem and devise coding algorithms for both cases. Each encoder sends some portion of its source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source and the hidden statistical dependence variables. This system performs close to the derived bounds when an appropriate doping rate is selected.
We concurrently develop techniques based on density evolution to analyze our coding algorithms. Experiments show that our models closely approximate empirical coding performance. This property allows us to efficiently optimize parameters of the algorithms, such as source degree distributions and doping rates.
We finally demonstrate the application of these adaptive distributed source coding techniques to reduced-reference video quality monitoring, multiview coding and low-complexity video encoding.
