Image compression based on logarithmic functions and pixels' shifting

Image compression has wide area in image processing researches. It consists of minimizing the size in bytes of an image without degrading the quality to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. For this reason, many researches and compression techniques have been proposed; some of these have been effective in some areas and failed in others. In this paper, we propose a novel approach based on logarithm transform with pixels shifting. Using a non-uniform quantization technique, this approach can adapt to the type of image, making it more robust than existing methods. To assess the usefulness of our approach, we evaluated its performance on several benchmark images, and compared it to that of state-of-the-art methods based on Discrete Cosine Transform and Discrete Wavelet Transform. Results of these experiments showed our approach to be more accurate than existing methods on the tested images.