Abstract
Noise degrades the performance of any image compression algorithm. This paper studies the effect of noise on lossy image compression. The effect of Gaussian, Poisson, and film-grain noise on compression is studied. To reduce the effect of the noise on compression, the distortion is measured with respect to the original image not to the input of the coder. Results of noisy source coding are then used to design the optimal coder. In the minimum-mean-square-error (MMSE) sense, this is equivalent to an MMSE estimator followed by an MMSE coder. The coders for the Poisson noise and the film-grain noise cases are derived and their performance is studied. The effect of this preprocessing step is studied using standard coders, e.g., JPEG, also. As is demonstrated, higher quality is achieved at lower bit rates.
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