Evaluating Multiscale Transform Based Image Compression Using Encoding Techniques

Abstract

Image compression is the emerging field to transmit the multimedia products like image, video and audio. Image compression is used to reduce the storage quantity as much as possible. The objective of this paper is to compare the multiscale transform based image compression encoding techniques. The multiscale transforms involved in this paper are wavelet transform, bandelet transform, curvelet transform, ridgelet transform and contourlet transform. Wavelet transform allows good localization both in time and frequency domain. Bandelet transform takes geometric regularity of the natural images to improve the efficiency of representation. Curvelet transform handles curve discontinuities well. Curvelets are the good tool for the analysis and the computation of partial differential equations. Curvelets also have micro local features which make them especially adapted to certain reconstruction problems with missing data. The ridgelet transform is the core idea behind curvelet transform. It is used to represent objects with line singularities. The contourlet transform gets smooth contours and edges at any orientation. It filters noise as well. The Encoding techniques involved in this paper are spatial orientation tree wavelet (STW), set partitioned embedded block (SPECK) and compression with reversible embedded wavelet (CREW). The performance parameters such as peak signal to noise ratio (PSNR), image quality index and structural similarity index (SSIM) are used for the purpose of evaluation. It is found that bandelet transform with all the encoding techniques work well.