Near-lossless image compression using an improved edge adaptive hierarchical interpolation

Abstract

Lossy image compression of medical images is required to store efficiently a huge amount of medical data on a remote storage device and to reduce transmission time of the image across a low-bandwidth communication. On the other hand, lossless compression of medical images is recommended because the loss of minor information leads to wrong medical diagnosis results that affects the life of patinets. To compromise the conflicting requirements of lossy and lossless image compression methods, a near-lossless image compression method is proposed.In the previous work, an edge adaptive hierarchical interpolation (EAHINT) algorithm was proposed for progressive lossless image compression. In this paper, EAHINT algorithm was enhanced for scalable near-lossless image compression. The proposed interpolation algorithm has three linear components, namely, one-directional, multi-directional and non-directional linear interpolators. The EAHINT algorithm swiches adaptively among the three linear interpolators based on the strength of the edge in a local context of the current pixel being predicted. The strength of the edge in local window was estimated using the variance of the pixels in the local window. Although the actual predictors are still linear functions, the switching mechanism tried to deal with non-linear structures like edges. Simulation results demonstrate that the improved interpolation algorithm has better compression ratio over the original EAHINT algorithm and JPEG-Ls image compression standard.