Adaptive Attention and Residual Learning for High-Fidelity Image Compression Using Deep Autoencoders

Image compression plays a pivotal role in reducing storage and transmission costs in multimedia systems. Traditional codecs often struggle to retain fine image details at low bitrates, leading to artifacts and perceptual degradation. To address this limitation, we propose a novel deep learning-based compression framework that combines adaptive attention mechanisms and residual learning within a deep autoencoder architecture