Prediction Of Optimal Operation Point Existence And Parameters In Lossy Compression Of Noisy Images

This paper deals with lossy compression of images corrupted by additive white Gaussian noise. For such images, compression can be characterized by existence of optimal operation point (OOP). In OOP, MSE or other metric derived between compressed and noise-free image might have optimum, i.e., maximal noise removal effect takes place. If OOP exists, then it is reasonable to compress an image in its neighbourhood. If no, more “careful” compression is reasonable. In this paper, we demonstrate that existence of OOP can be predicted based on very simple and fast analysis of discrete cosine transform (DCT) statistics in 8x8 blocks. Moreover, OOP can be predicted not only for conventional metrics as MSE or PSNR but also for visual quality metrics. Such prediction can be useful in automatic compression of multi- and hyperspectral remote sensing images.