Medical image compression based on vector quantization and discrete wavelet transform.

Image compression is the latest trend in the entire world. Images can be compressed to reduce their file sizes, which in turn reduces the time it takes to upload and download them to a website and the amount of space they take up on a server. The goal of image compression is to reduce the amount of data needed to represent a image digitally and to do it at a low bit rate without significantly diminishing the image quality. The image can be restored from its compressed state. One way to informally categorize image compression methods is by whether the original image can be faithfully reconstructed from the compressed version. This are what we call a lossless technique and a lossy technique, respectively. Ultrasound, CT scans, and MRI images are used as part of the datasets for this investigation. As chronic diseases are increasingly prevalent, there has been a substantial increase in the number of diagnostic images produced. annually employed technology. But, storage is a major issue since there isn't enough capacity to save all of these expanding medical imagegraphs. Vector quantization in addition to two level wavelet transform used in this work to achieve the aim. Multiple evaluation technique is used to evaluate the results which are PSNR, MSE, RMSE, SSIM. [ABSTRACT FROM AUTHOR]