Novel Mathematical/Computational Approaches to surveillance

Current algorithms for data compression are founded on three basic principles:transformation, quantization, and entropy coding. The transformation attempts to remove statistical redundancies from the input, thereby reducing the data to a smaller more manageable set. The quantizers convert samples into a finite set of levels, typically in a way that minimizes some predefined error measure (e.g. mean-squared, mean absolute difference, etc). The entropy coder converts the quantizer output, which is in the form of a sequence of level values, into a bit string. Assignments are made with variable length codes in an attempt to minimize the overall average bit rate of the system.An important first step of this work is to formally benchmark the performance of current state-of-the-art algorithms in the context of SAR image compression. Specifically, this benchmarking is performed in terms of visual quality and ATR performance quality. This is in direct contrast to mean-square error (MSE) and peak signal to noise ratio (PSNR) measures that are commonly used to assess image coding quality in the compression community. This important first step is done in recognition that the true measure of merit for defense applications is the preservation of target classification information after transmission.