Lossy compression of images corrupted by film grain noise

Noise degrades the performance of any image compression algorithm. This paper studies the effect of film-grain noise on lossy image compression. Since the goal of lossy image compression algorithms is to achieve the best fidelity for a given bit rate, the distortion is measured with respect to the original image not with respect to the input to the coder. The results of noisy source coding are used to develop this coder. The minimum-mean-squared-error (MMSE) coder involves MMSE restoration of the noisy image. This paper presents an MMSE image restoration algorithm based on modeling the image as a Markov random field. The performance of this preprocessing step is also studied when using JPEG