Embedded Image Compression Techniques Combined with PCA to Improve Compression Efficiency

The compression of medical images receives a lot of interest due to the need to reduce their size without compromising the diagnostically important medical information included in the images. When patient images are transferred from one expert group to another in biomedical research, compression becomes essential. The proposed image compression method is helpful in reducing the amount of storage space required for medical systems. The PCA algorithm can assist with image compression. PCA formulation is one possible low-loss digital image compression algorithm. During this study, it has been discovered that PCA can be applied to image compression, image fusion, image registration, image segmentation, or Feature extraction, noise reduction, and redundant information elimination. However, PCA is mainly used for image compression and feature extraction. This paper compares and contrasts various embedded image compression methods using PCA. To improve compression performance, EZW employs a lossy technique that is introduced by PCA. To improve the compression performance, the PCA introduces a lossy technique, which EZW then uses. The reconstructed image obtained using PCA techniques does not have a sufficient Peak Signal to Noise Ratio (PSNR) value, which can be further enhanced using a different technique known as EZW. According to the proposed PCA-EZW results, the method excels compared to other recent state-of-the-art techniques while causing a feasible loss in image quality.