Lossy compression of images corrupted by mixed Poisson and additive Gaussian noise

Abstract

Lossy compression of images corrupted by mixed Poisson and additive Gaussian noise is considered. Peculiarities of noise filtering effects observed for two approaches to image compression are studied. This is first done for specially created artificial image and, then, for two standard test images. It is shown that the standard (direct) approach to lossy compression leads to different degrees of noise suppression depending upon image mean in homogeneous regions. The use of root transformation leads to better results. Moreover, coder parameters can be adjusted to mixed noise parameters if they are known a priori. Then it becomes possible to reach optimal operation point (OOP), i.e., such compression ratio (CR) or bpp that provide maximal peak signal-to noise ratio (PSNR) or minimal MSE calculated between compressed and noise free images without having an actual noise-free image. A provided CR can be about 10⋯30 depending upon image complexity.