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Image compression can improve the performance of the digital systems by reducing time and cost in image storage and transmission without significant reduction of the image quality. For image compression it is desirable that the selection of transform should reduce the size of resultant data set as compared to source data set. EZW is computationally very fast and among the best image compression algorithm known today. This paper proposes a technique for image compression which uses the Wavelet-based Image Coding in combination with Huffman encoder for further compression. A large number of experimental results are shown that this method saves a lot of bits in transmission, further enhances the compression performance. This paper aims to determine the best threshold to compress the still image at a particular decomposition level by combining the EZW encoder with Huffman Encoder. Compression Ratio (CR) and Peak-Signalto-Noise (PSNR) is determined for different threshold values ranging from 6 to 60 for decomposition level 8.

Medical image compression applications are quality-driven applications which demand high quality for certain regions that have diagnostic importance in an image, where even small quality reduction introduced by lossy coding might alter subsequent diagnosis, which might cause severe legal consequences. Due to this, lossless techniques have been extensively used. As an alternative, owing to the observation that only some part of the image actually is of interest to the practitioners, ROI-based techniques are becoming popular. This paper proposes four techniques for this purpose. The four techniques are based on Mixed Raster Content layering, block-based thresholding, region growing and active contour algorithms. All the four techniques are enhanced and have the common objective of determining a ROI that can improve the compression process. Experiments results prove that all the four algorithms are efficient in determining the ROI and are efficient in terms of segmentation, compression and speed.

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