<title>Region-based prediction method for structural representation and compression coding of images</title>

Abstract

Noise greatly degrades the quality of the image and the performance of any image compression algorithm. This paper presents an approach to representation and compression of noisy images. A new concept RBP region-based prediction model is first introduced, and then the RBP model is utilized to work on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of the individual pixels surrounding the pixel to be processed. The RBP model allows for the regions instead of the individual pixels surrounding the pixel to be predicted. A practical algorithm for implementation of RBP is developed. In our experiments, the practical algorithm is applied to noisy synthetic images. By encoding we achieve the bit rate 1.10 bits/pixel of the noisy synthetic image. The decompressed image achieves the peak SNR 42.59dB. Compared with the peak SNR 41.01dB of the noisy synthetic image, the decompressed synthetic image is better in the MSE sense. The image compression standard JPEG provides peak SNR 33.17dB for the noisy synthetic image at the same bit rate, and the conventional median filter with 3 by 3 window provides the peak SNR 25.89dB. The RBP model is also applied to a medical MRI image. The subjective evaluation of the decompressed MRI image shows that our method is superior to the conventional methods.