Journals Proceedings

Abstract

There are various applications of image processing like satellite imaging, biomedical imaging, remote sensing and radar imaging where the size of the image and quality of the image is most important but it requires a lot of space to store at the places due to the high bandwidth of the communication of the original image. In these applications we apply the image compression techniques to store the data and reduce its space for storage time. There are various factors which affecting the compression like spatial resolution, bit depth, noise, image sizing, viewing distance, etc. Biomedical imaging focuses on the capture of images for both diagnostic and therapeutic purpose. The biomedical images can be displayed by the high bit resolution and we have to convert the high bit resolution into the low bit resolution for displaying the images. This problem is occurs mostly on the low-cost or small devices. In this paper, we capture the 2D images for resolving. The resolution of the 2D images is very high so it takes more space for storage. If the size of the image is very high then it is not easy to send the image without compression. We focus on the 2D images compression which convert the high bit resolution into low bit resolution and we compress the 2D biomedical images with the help of the Discrete Cosine Transform (DCT) and apply the Lempel-Ziv-Welch (LZW) lossless image compression technique.