Optimization of a new digital image compression algorithm based on nonlinear dynamical systems

In this paper we discuss the formulation, research and development of an optimization process for a new compression algorithm known as DYNAMAC, which has its basis in the nonlinear systems theory. We establish that by increasing the measure of randomness of the signal, the peak signal to noise ratio and in turn the quality of compression can be improved to a great extent. This measure, entropy, through exhaustive testing, will be linked to peak signal to noise ratio (PSNR, a measure of quality) and by various discussions and inferences we will establish that this measure would independently drive the compression process towards optimization. We will also introduce an Adaptive Huffman Algorithm to add to the compression ratio of the current algorithm without incurring any losses during transmission (Huffman being a lossless scheme)