Image compression is the ability of representing the image in a compressed form rather than its original form. An image is a visual perception of a subject or surrounding. But digitally, it is an organization of small building blocks called as pixels. In this paper the compression is done by two level processes, initially a Haar wavelet decomposition is applied then rough set theory Technique is applied for best compressing image. The ASWDRT makes use of adaptive scanning order to estimate locations of new significant image values which leads to enhancement of edge resolution in compressed images. The performance measure of compression method is done with following parameters like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Compression Ratio (CR). The quantitative evolution and comparison is done with some popular compression methods like Set Partitioning in Hierarchical Trees (SPIHT), Embedded Zero Tree Wavelet (EZW) and Wavelet Difference Reduction (WDR). The proposed method shows superior performance in terms of CR. The abovementioned Compression techniques are developed and performance parameters are calculated using MATLAB 2014.

Rough set has capability to handle the uncertainty there in the data. This characteristic of RST makes it a suitable to obtain Edge and Edge Class information from the noisy image. The edge information and class information in order increase the performance of further diagnostic process. The obtained edge map is found to be continuous and closed and is capable of defining object boundaries even in noisy image. It appeared to be defining object boundaries in a better way compared to a couple of existing methods such as Canny Edge Detector and Active Contour methods.

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