Compression of image sequences in interactive medical teleconsultations

Interactive medical teleconsultations are an important tool in the modern medical practice. Their applications include remote diagnostics, conferences, workshops and classes for students. In many cases standard medium or low-end machines are employed and the teleconsultation systems must be able to provide high quality of user experience with very limited resources. Particularly problematic are large datasets, consisting of image sequences, which need to be accessed fluently. The main issue is insufficient internal memory, therefore proper compression methods are crucial. However, a scenario where image sequences are kept in a compressed format in the internal memory and decompressed on-the-fly when displayed, is difficult to implement due to performance issues. In this paper we present methods for both lossy and lossless compression of medical image sequences, which require only compatibility with Pixel Shader 2.0 standard, which is present even on relatively old, low-end devices. Based on the evaluation of quality, size reduction and performance, the methods are proved to be suitable and beneficial for the medical teleconsultation applications

Correlation modeling for compression of computed tomography images

Abstract-Computed Tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3D images that aid medical diagnosis. Previous approaches for coding such 3D images propose to employ multi-component transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this work, we propose a novel analysis which accurately predicts when the use of a multi-component transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multi-component transforms are appropriate for images with correlation coefficient r in excess of 0.87

Memory-efficient lossless video compression using temporal extended JPEG-LS and on-line compression

Use of temporal predictors in lossless video coders play a significant role in terms of compression gain, but comes with a cost of significant memory requirement since this approach requires to save at least one frame in buffer for residue calculation. An improvement to standard JPEG-LS based lossless video coding algorithm is proposed in this work which requires very small amount of memory comparing to the regular approach keeping the computational complexity low. To obtain a higher compression, a combination of spatial and temporal predictor model has been used where appropriate mode is selected adaptively on a pixel based analysis. Using only one reference frame, the context based temporal coder performs its calculation regarding mode selection and prediction error calculation with already reconstructed pixels. This method eliminates the overhead of transmitting the coding mode in the decoder side. The need for storage space to save the only reference frame is further reduced by introducing on-line lossy compression on that frame. Relevant pixels from the stored reference frame are obtained by partial on-the-fly decompression. The combination of temporally extended context based prediction and on-line compression achieves a significant gain in compression ratio comparing to standard frame-by-frame JPEG-LS video coding keeping the memory requirement low, making it usable as a lightweight lossless video coder for embedded systems