Frequency estimation for complex field image channel coding

BCH (Bose-Chaudhuri-Hocquenghem) codes over complex fields in the frequency domain have been proposed for image coding applications as robust channel coding methods. The embedded problem of estimation of complex sinusoids corrupted by white additive noise may be solved in different ways. A standard approach for modelling such signals employs low-order nearly nonstationary autoregressive (AR) models with complex parameters. In the paper linear prediction/least squares based methods are used for parameter estimation including forward linear prediction (FLP) and forward-backward linear prediction (FBLP). The two parameter estimation methods have been applied to the first- and the second-order AR models with minimal number of samples to obtain efficient frequency estimators for decoding of BCH codes. Computer simulation has been carried out to compare different frequency estimation algorithms with regards to three significant criteria: frequency resolution, computing complexity and coding redundancy. The results show that different algorithms exhibit optimal combinations of characteristics for different constraints and importance of criteria.