An efficient trapezoidal compression algorithm using wavelet transformation for medical image

Abstract

Image compression processes moderate the number of bits essential to signify an image, which can improve the performance of systems during storage and transmission without compromising image quality. They are classified into lossy compression and lossless compression. In this work, a new algorithm called trapezoidal algorithm has been proposed for image compression. The encoding part of proposed algorithm is like trapezoid shape so it is named as trapezoidal algorithm.  Two transformation techniques DWT, IWT with three wavelets such as Haar, Sym4 and Coif1 have been combined for image compression to confer good characteristics of these methods. In this each pixel coordinates are encoded using the logic from SPECK algorithm. In SPECK, the set S is encoded when the pixel level is reached whereas in proposed algorithm (trapezoidal) the set S is encoded when the size of set S is reached 4x4. The approximation of image is named as set S. set S can be formed into three subset termed as s1, s2, s3. S is grouped into many subsets each set can be defined based on a pattern of proposed algorithm. Magnetic Resonance Imaging (MRI) of brain and Computer Tomography (CT) of lung images are used for analyzing compression. The proposed algorithm gives high PSNR compared to existing algorithms EZW, SPIHT and SPECK. The performance metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Mean square error (MSE), Bits Per Pixel (BPP), Compression Ratio (CR) and Compression Time (CT) are measured for lung and brain images. The dataset has been collected from various scan centers.