Final Report on Evaluation of Synthetic Aperture Radar (SAR) Image Compression Techniques

With the improvement of synthetic aperture radar (SAR) technology, larger areas are being imaged and the resolution of the images has increased. Larger images have to be transmitted and stored. Due to the limited storage and/or downlink capacity on the airplane or satellite, the volume of the data must be reduced. This makes compression of SAR images with minimal loss of information important. Mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are the commonly quoted performance measures for comparing the compression algorithms. However, these measures inherently assume that the distortion is image independent noise, which is not a valid assumption in image compression algorithms. We propose a way to measure the distortion caused by compression and decompression of an image, by decoupling the distortion into a linear e ect and additive uncorrelated noise, which models the nonlinear distortion. Using this procedure, the linear frequency distortion can be quanti ed by a weighted mean of the deviation from an all-pass system. The noise can be weighted according to a speci c application before measuring the signal to noise ratio. Since the nonlinear distortion, such as blocking e ect and mosquito noise, is a high frequency e ect, we use a discrete Laplacian operator to emphasize higher frequencies in the image and use a measure correlation measure to quantify this distortion. Our simulation results show that the proposed metrics are consistent with image quality.