Region-based prediction coding for compression of noisy synthetic images

Noise greatly degrades the image quality and performance of image compression algorithms. This paper presents an approach for the representation and compression of noisy synthetic images. A new concept region-based prediction (RBP) model is first introduced, and then the REP model is utilized on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of individual pixels surrounding the pixel to be processed. The REP model uses regions instead of individual pixels as the context for prediction. An algorithm for the implementation of REP is proposed and applied to noisy synthetic images in our experiments. Using REP to find the residual data and encoding them, we achieve a bit rate of 1.10 bits/pixel for the noisy synthetic image. The decompressed image achieves a peak SNR of 42.59 dB. Compared with a peak SNR of 41.01 dB for the noisy synthetic image, the quality of the decompressed synthetic image is improved by 1.58 dB in the MSE sense. In contrast to our proposed compression algorithm with its improvement in image quality, conventional coding methods can compress image data only at the expense of lower image quality. At the same bit rate, the image compression standard JPEG provides a peak SNR of 33.17 dB for the noisy synthetic image, and the conventional median filter with a 3x3 window provides a peak SNR of 25.89 dB.