Master of Science / Department of Electrical and Computer Engineering / D. V. Satish Chandra / The future of healthcare delivery systems and telemedical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing and communication techniques. E-health was born with the integration of networks and telecommunications when dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis. Healthcare systems in recent years rely on images acquired in two dimensional (2D) domain in the case of still images, or three dimensional (3D) domain for volumetric images or video sequences. Images are acquired with many modalities including X-ray, positron emission tomography (PET), magnetic resonance imaging (MRI), computed axial tomography (CAT) and ultrasound. Medical information is either in multidimensional or multi resolution form, this creates enormous amount of data. Efficient storage, retrieval, management and transmission of this voluminous data is extremely complex. One of the solutions to reduce this complex problem is to compress the medical data losslessly so that the diagnostics capabilities are not compromised. This report proposes techniques that combine integer transforms and predictive coding to enhance the performance of lossless compression. The performance of the proposed techniques is evaluated using compression measures such as entropy and scaled entropy.

Lossless image compression has many applications, for example, in medical imaging, space photograph and film industry. In this thesis, we propose an efficient lossless image compression scheme for both binary images and gray-scale images. The scheme first decomposes images into a set of progressively refined binary sequences and then uses the context-based, adaptive arithmetic coding algorithm to encode these sequences. In order to deal with the context dilution problem in arithmetic coding, we propose a Lloyd-like iterative algorithm to quantize contexts. Fixing the set of input contexts and the number of quantized contexts, our context quantization algorithm iteratively finds the optimum context mapping in the sense of minimizing the compression rate. Experimental results show that by combining image decomposition and context quantization, our scheme can achieve competitive lossless compression performance compared to the JBIG algorithm for binary images, and the CALIC algorithm for gray-scale images. In contrast to CALIC, our scheme provides the additional feature of allowing progressive transmission of gray-scale images, which is very appealing in applications such as web browsing.

Lossless image compression has many applications, for example, in medical imaging, space photograph and film industry. In this thesis, we propose an efficient lossless image compression scheme for both binary images and gray-scale images. The scheme first decomposes images into a set of progressively refined binary sequences and then uses the context-based, adaptive arithmetic coding algorithm to encode these sequences. In order to deal with the context dilution problem in arithmetic coding, we propose a Lloyd-like iterative algorithm to quantize contexts. Fixing the set of input contexts and the number of quantized contexts, our context quantization algorithm iteratively finds the optimum context mapping in the sense of minimizing the compression rate. Experimental results show that by combining image decomposition and context quantization, our scheme can achieve competitive lossless compression performance compared to the JBIG algorithm for binary images, and the CALIC algorithm for gray-scale images. In contrast to CALIC, our scheme provides the additional feature of allowing progressive transmission of gray-scale images, which is very appealing in applications such as web browsing.

The aim of this master's thesis was to design, develop and test a method for lossless image compression. The theoretical part includes a description of selected exiting methods such as RLE, MTF, adaptive arithmetic coding, color models used in LOCO-I and JPEG 2000, predictors MED, GAP and laplacian pyramid. The conclusion includes a comparison of various combinations of chosen approaches and overall efficiency compared with PNG and JPEG-LS.