Medical Image Compression Using Block-to-Row Principal Component Analysis (BTRPCA)

Abstract

Principal Component Analysis (PCA) is capable of completely decorrelating input data in the transform domain. However, PCA is limited in image compression because there is a need to transmit or store the eigenvectors of the input data over a communication link and thereby affects the rate-distortion performance. In an effort to improve rate-distortion performance, this work proposed a block-to-row PCA (BTRPCA) algorithm that employs the eigenvectors from the model image of the same image modality coupled with a row vectorization approach. It is found from this work that the proposed method achieves PSNR improvements of up to 10 dB compared to its PCA counterparts at compression ratio of 64:1. At the same compression ratio, the proposed BTRPCA managed to achieved PSNR of 40 dB while the comparing algorithms scores well below 40 dB at the same configuration. This approach successfully improves the rate-distortion performance by reducing the overwhelming side information and computation overhead associated with PCA.