An Efficient Rank-based Image Transformation Scheme using Entropy Coding in Gray-Level Images

In this paper, we propose a rank-based image transformation scheme which is a pre-processing method for enabling more efficient compression of gray-level images by entropy encoder. For this, before we do entropy encoding on a stream of gray-level values in an image, the proposed method counts co-occurrence frequencies for neighboring pixel values. Then, it replaces each gray value with particularly ordered numbers based on the co-occurrence frequencies. Finally, the ordered number are transmitted to an entropy encoder. The pre-processing step enhances the statistical characteristic of the image transformation and thus improves the performance of entropy coding considerably. The result from our simulation using 8 bits gray-scale images shows that the proposed method can reduce bit rate by up to 37.85% compared with existing plain entropy coders.