Enhancing Learned Image Compression with Gaussian Mixture Models and Deep Neural Networks

Traditional image compression standards such as JPEG, JPEG2000, and BPG have achieved notable success, yet struggle to meet the low-latency and adaptive demands of modern wireless transmission. Their fixed transform coding frameworks are ill-suited for dynamic wireless environments. Recent advances in deep learning, particularly Convolutional Neural Networks and Recurrent Neural Networks, have enabled end-to-end nonlinear modeling for improved image compression. This study investigates the integration of CNN- and RNN-based architectures into wireless image transmission systems, targeting two key challenges: reducing perceptual distortion and optimizing computational efficiency. A core contribution lies in introducing Gaussian Mixture Models (GMMs) into these DL frameworks, enabling probabilistic modeling of latent features to support adaptive bit allocation. By comparing with traditional and existing DL-based methods, the proposed approach offers dual optimization in compression performance and channel adaptability. Experimental results show that GMM-enhanced DL models significantly improve robustness and compression quality under fluctuating channel conditions, offering a promising direction for deploying adaptive, efficient image compression schemes in resource-constrained wireless networks.