Data compression of discrete sequence: A tree based approach using dynamic programming

A dynamic programming based approach for data compression of a ID sequence is presented. The compression of an input sequence of size N to that of a smaller size k is achieved by dividing the input sequence into k subsequences and replacing the subsequences by their respective average values. The partitioning of the input sequence is carried with the intention of reducing the mean squared error in the reconstructed sequence. The complexity involved in finding the partitions which would result in such an optimal compressed sequence is reduced by using the dynamic programming approach, which is presented.