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.

This paper deals with an efficient image segmentation algorithm for video images which is quite useful for video based traffic surveillance applications. It includes video segmentations, morphological operations and labeling. In the field of surveillance system, effective video object segmentation is a conveyance for video analysis and processing. It presents a new algorithm for video object segmentation i.e. unsupervised image segmentation based on Gaussian mixture model with modified EM procedure. It uses the spatial unsupervised GMM clustering technique in which the objective function is modified or the prior term is added in the Bayesian. Firstly we use EM algorithm to estimate the distribution of input image data with which the number of mixture components is automatically determined. Secondly the segmentation is arrived at by clustering each pixel into the appropriate component according to the Minimum Message Length (MML) criterion with the help of appropriate priors like Dirichlet-Normal-Wishart (DNW) prior. The proposed technique automatically decides the best number of clusters for images. The best number of clusters is obtained by using the cluster validity criterion with the help of Gaussian distribution.