This paper proposes a novel image compression algorithm using curvelet transform. The original image was decomposed into curvelet coefficients using fast discrete curvelet transform, after that the different scales of quantized curvelet coefficients were selected for lossy compression and arranged in descending order. Then we set the cutoff threshold for curvelet coefficients. The proposed method was compared with image compression method based on wavelet transform. Experimental results show that the compression performance of our method gains much improvement based on PSNR. Moreover, the algorithm works fairly well for declining block effect at higher compression ratios.

In this paper, an efficient approach for medical image segmentation based on Skew Gaussian distribution using EM algorithm is proposed. It is necessary to classify the brain voxels into one of the 3 main tissues mainly Gray matter (GM), White matter (WM) and Cerebro Spinal fluid (CSF) in any brain MRI image. Quantization of Gray & White matter is a topic of concern in neuro-degenerative disorders. Viz., Alzheimer disease and Parkinson’s diseases. Hence, it is necessary to identify the tissue more efficiently. In this approach we used Skew Gaussian distribution to classify the tissue voxels and the updated parameters are obtained using EM algorithm. The outputs generated are evaluated using the medical image quality metrics. Experimentation is carried out on two different T1 weighted brain images.

Synthetic Aperture Radar (SAR) System generates the large volume of the data and the ability to transmit it to the ground, or to store it, is not increasing as fast, due to practical constraints imposed in the system design. This shortfall prompts interest in compression-decompression strategies for rapid transmission of images. Transform coding based on the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) is well understood for optical images but has not been well studied for SAR Images. Wavelets have been introduced as a signal-processing tool and they are widely used in image compression applications. The transform in wavelet and multiwavelet domain is capable of compacting the energy of image into a small number of coefficients, localized in both space and frequency. But the wavelet transform has got more importance due to its manifold characteristics i.e. high compression ratio, multi-resolution in nature, use of different basis functions that lead to the desirable property of characterizing and localizing signal features in frequency domains. In this paper, we have evaluated the performance of Discrete Cosine Transform, Block Truncation Coding (BTC), Gaussian Pyramidal (GP) and Multiwavelet Transformation (MWT). Mean squared Error (MSE), Maximum Absolute Error (MAE), Signal to Noise Ratio (SNR), Peak signal to noise ratio (PSNR), Compression Ratio (CR), is used as objective performance criteria. Based on the observation of the above performance evaluation system, the promising result has been depicted i.e on an average compression 70% to 77% and RE 96.Objective of exploiting features MWT for compression of SAR images has been shown.