  In this work, we propose a two-stage autoencoder based
compressor-decompressor framework for compressing malaria RBC cell image
patches. We know that the medical images used for disease diagnosis are around
multiple gigabytes size, which is quite huge. The proposed residual-based dual
autoencoder network is trained to extract the unique features which are then
used to reconstruct the original image through the decompressor module. The two
latent space representations (first for the original image and second for the
residual image) are used to rebuild the final original image. Color-SSIM has
been exclusively used to check the quality of the chrominance part of the cell
images after decompression. The empirical results indicate that the proposed
work outperformed other neural network related compression technique for
medical images by approximately 35%, 10% and 5% in PSNR, Color SSIM and MS-SSIM
respectively. The algorithm exhibits a significant improvement in bit savings
of 76%, 78%, 75% & 74% over JPEG-LS, JP2K-LM, CALIC and recent neural network
approach respectively, making it a good compression-decompression technique.
