High speed camera systems produce large volumes of image data every second that are stored on its onboard storage. To operate these cameras over a long period of time, the data from its storage has to be transmitted to an external storage in real-time. This necessitates a fast and memory-efficient lossless image compression algorithm implemented in hardware. Although JPEG-LS, a lossless image compression scheme is considered to provide adequate compression performance at reasonable computational complexity, it makes intensive use of a context modelling scheme which typically results in an expensive (regarding speed and memory) module in a hardware implementation. The sequential processing of pixels in the raster scan order makes the speed of operation inadequate for high input data rate, e.g. 256-by-256 pixels/frame at very high FPS (frames per second). This thesis proposes a heuristic approach to improve the speed and memory efficiency of the hardware implementation by modifying the context update of JPEG-LS. Additionally to improve the speed of processing an image, a series of encoder units work on different parts of the image in parallel. The proposed modifications are tested by both software simulations in C++ and VHDL synthesis. While simulation results show compression ratio closely comparable to the original JPEG-LS, synthesis results show an improvement over JPEG-LS in terms of hardware usage and speed of operation.