high performance computing on graphics processing units: hgpu.org

Compression is used for storage related applications that offers compression of audio/video, executable program, text, source code and so on. While compressing images into smallest space as possible, the constraint lies in the multispectral form of data with continuous images. In such a scenario, efficient lossless image compression is required such that the compression ratio can be improved and reduces the computational complexity. In this paper, we proposed a model called, Coefficient-based Discrete Wavelet Transform (CDWT) for lossless image compression which improves the compression ratio and reduces the computational complexity involved during transformation. The Coefficient-based Discrete Wavelet Transform initially partitions the image into coefficients to decide upon which coefficient value to be considered for encoding. Next, Probability-based Transformation for lossless image compression for continuous images follows Probability-based encoding to reduce the computational complexity involved during transformation. Extensive experiments carried out on the Waterloo color images have revealed the outstanding performance of the proposed CDWT model when benchmarked with various well established state-of-the-art schemes. The results obtained by CDWT witness a significant increase in compression ratio by reducing the total error while compressing with minimized computational complexity when compared with the results produced by the other state-of-the art methods considered.