Image Compression Using Neural Networks

Neural networks have demonstrated remarkable capabilities in diverse applications including data compression and communication systems. This paper presents an enhanced Convolutional Autoencoder (CAE) architecture specifically designed for efficient image compression in communication networks, where bandwidth utilization and transmission efficiency are critical concerns. Using the MNIST dataset, our optimized CAE reduces image dimensions to a compact latent space representation, where the encoder extracts critical features enabling accurate reconstruction while significantly reducing bandwidth requirements for data transmission. Our novel contributions include: an optimized CAE architecture with residual learning for improved reconstruction quality and a comprehensive performance evaluation framework for compression systems in communication networks. The model achieves compression ratios of $\mathbf{1 2. 2 5: 1}$, substantially outperforming JPEG (1.41:1) while maintaining high structural similarity (SSIM) $\gt 95 \%$ and peak signal-to-noise ratio (PSNR) $\gt 36 \mathrm{~dB}$. We analyze computational complexity and demonstrate how our approach can be efficiently deployed in bandwidth-constrained communication systems, providing an adaptive compression mechanism where compression levels and image quality can be fine-tuned according to network conditions. This research addresses a critical need in modern communication infrastructure: efficient data representation for transmission across bandwidthlimited channels.