Graph-based transforms based on prediction inaccuracy modeling for pathology image coding

Abstract

Digital pathology images are multi giga-pixel color images that usually require large amounts of bandwidth to be transmitted and stored. Lossy compression using intra - prediction offers an attractive solution to reduce the storage and transmission requirements of these images. In this paper, we evaluate the performance of the Graph - based Transform (GBT) within the context of block - based predictive transform coding. To this end, we introduce a novel framework that eliminates the need to signal graph information to the decoder to recover the coefficients. This is accomplished by computing the GBT using predicted residual blocks, which are predicted by a modeling approach that employs only the reference samples and information about the prediction mode. Evaluation results on several pathology images, in terms of the energy preserved and MSE when a small percentage of the largest coefficients are used for reconstruction, show that the GBT can outperform the DST and DCT