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

Abstract

<span>In medical and scientific imaging, lossless image compression is recommended because the loss of minor details subject to medical diagnosis can lead to wrong diagniosis. On the other hand, lossy compression of medical images is required in the long run because a huge quantity of medical data needs remote storage. This, in turn, takes long time to search and transfer an image. Instead of thinking lossless or lossy image compression methods, near-loss image compression mehod can be used to compromise the two conflicting requirements. In the previous work, an edge adaptive hierarchical interpolation (EAHINT) was proposed for resolution scalable lossless compression of images. In this paper, it was enhanced for scalable near-less image compression. The interpolator of this arlgorithm swiches among one-directional, multi-directional and non-directional linear interpolators adaptively based on the strength of the edge in a 3x3 local casual context of the current pixel being predicted. The strength of the edge in local window was estimated using the variance of the 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 the exsisting the original EAHINT algorithm and JPEG-Ls image compression standard. </span>

Symmetric quadratic tetration interpolation using forward and backward operation combination

“…In order to obtain the unknown data from its related known data, interpolation is an important method. It has been widely applied in several applications, i.e., signal reconstruction [1]- [9], signal and image denoising [10]- [12], medical image processing [13]- [18], image resizing [19]- [28], image classification [29], image compression [30], image enhancement [31], change detection [32], video processing [33], and more [34], [35]. The interpolation is used to generate the new data points from the discrete set of known data points in case of its position is within the range of known data.…”