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Problem statement: Image processing applications were drastically increasing over the years. In such a scenario, the fact that, the digital images need huge amounts of disk space seems to be a crippling disadvantage during transmission and storage. So, there arises a need for data compression of images. Approach: This study proposed a novel technique called binary merge coding for lossless compression of images. This method was based on spatial domain of the image and it worked under principle of Inter-pixel redundancy reduction. This technique was taken advantage of repeated values in consecutive pixels positions. For a set of repeated consecutive values only one value was retained. Results: The proposed binary merge coding achieved the compression rate of the brain image was 1.6572479. Comparatively, it is 100% more than the compression rate achieved by standard JPEG. Conclusion/Recommendations: This technique was simple in implementation and required no additional memory area. The experimental results of binary merge coding were compared with standard JPEG and it showed that, the binary merge coding improved compression rate compared to JPEG. The same algorithm can be extending to color images. This algorithm can also used for lossy compression with few modifications.

The proposed paper is to develop an efficient compression scheme and to obtain better quality and higher compression ratio through discrete curvelet transform and embedded coding of curvelet coefficients through improved Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm. The paper demonstrates a significant improvement in visual quality and faster encoding and decoding than the wavelet with SPHIT compression. The SPHIT with wavelet compression fail to represents discontinuous along the curves. The curvelet transform is a multiscale directional transform, which allows an almost optimal non adaptive sparse representation of objects with edges. By using improved SPHIT with Curvelets model the transform coefficients based on probability of significance, at a fixed threshold of the offspring. As far as objective quality assessment of the image compression of the proposed work will gives improved Peak Signal to Noise Ratio (PSNR) and high compression ratio (CR) compared with the existing wavelet transform with SPHIT image compression.

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