Abstract
Image Compression Based on Cubic Bezier Interpolation, Wavelet Transform, Polynomial Approximation, Quadtree Coding and High Order Shift Encoding
Highlights
By entering the digital age, the world has faced a vast amount of information
The objective of this study is to develop an efficient image compression system using two types of surface representation, Cubic Bezier Interpolation (CBI) is applied on the whole area of the image to compensate the low variation components may exist in the image, the produced cubic Bezier surface is subtracted from the image signal to get the residue component, the produced residue is decomposed using bi-orthogonal wavelet transform to transform the pixels in the residue image into frequency domain coefficients
Many sets of tests have been performed to assess our work in termsof Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Encoding Time (ET) and Decoding Time (DT).As image test samples Lena and Barbara image have been used
Summary
By entering the digital age, the world has faced a vast amount of information. Dealing with this vast amount of information can often lead to numerous difficulties. Digital information must be stored, analyzed, retrieved and processed in an effective way, so as to be put to practical use (Raid et al, 2014). Uncompressed multimedia (graphics, video and audio) data requires very high bandwidth and considerable storage capacity in transfer. In order to manage large data objects efficiently, these objects need to be compressed to reduce the file size (Tripathi, 2014). During the last several years, wavelet transformations have achieved widespread acceptance, within image compression research. Wavelets are chosen as the basic function in JPEG 2000 (Johnsen and Standeren, 2005)
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