Hardware Implementation of JPEG-LS codec

The primary goal of this thesis is to implement a hardware version of the JPEG-LS, or JPEGLossless, image compression algorithm in VHDL. The JPEG-LS algorithm is currently the designated standard for lossless compression of grayscale and color images by the JPEG committee. Although lossy image compression is widely used when dealing with grayscale images, there are some applications that require lossless image compression so that the original image may be recovered. This is often the case for historical and legal document image archives, medical and satellite imagery, and biometric images. The JPEG-LS algorithm is much less complex than other current lossless image compression algorithms and offers similar or better compression gains. Near-lossless compression offers higher compression gains by using a pixel tolerance specified by the user. The algorithm uses a predictive technique for compression, and the resulting prediction error is encoded, not the pixel value itself. This prediction error is encoded with Golomb-Rice coding, which is optimal for a geometric distribution such as prediction error. The predictor enters a special run-length mode to encode pixels with identical values in lossless mode (or nearly identical values within a known value in near-lossless mode), which maximizes compression further. In this thesis, the JPEG-LS algorithm is implemented in C, VHDL, and further synthesized using the Synopsys synthesis tool suite. Pictorial, document, medical, remote sensing, and biometric images are used for testing the project against another standard-compliant software implementation. The compression ratio for lossless compression is approximately 2 and is greater for near-lossless compression. The end result is a Synopsys schematic that represents a JPEG-LS codec, which is capable of lossless and near-lossless encoding and decoding. Performance characteristics such as chip area, speed, and power consumption are extracted from the synthesis tool. These are approximately 375,000 gates, a 15 ns clock cycle, and 59 mW respectively. A hardware implementation of this algorithm on an FPGA or ASIC would give a digital camera or scanner an edge in the marketplace

Information preserved guided scan pixel difference coding for medical images

This paper analyzes the information content of medical images, with 3-D MRI images as an example, in terms of information entropy. The results of the analysis justify the use of Pixel Difference Coding for preserving all information contained in the original pictures, lossless coding in other words. The experimental results also indicate that the compression ratio CR=2:1 can be achieved under the lossless constraints. A pratical implementation of Pixel Difference Coding which allows interactive retrieval of local ROI (Region of Interest), while maintaining the near low bound information entropy, is discussed.Comment: 5 pages and 5 figures. Published in IEEE Wescanex proceeding

Contributions to HEVC Prediction for Medical Image Compression

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC