End-to-End Learned Image Compression: Approaches, Deployment, and Standardization
Seminar paper, Chair of Media Technology, Technical University of Munich · 2026
A review of learned image compression (LIC): the VAE-based framework most models build on, advances in transforms, quantization and entropy modeling, and perceptual compression beyond rate-distortion. Representative models are benchmarked on a Tesla T4 GPU to assess practical deployability, and the JPEG AI standard is examined. Standardization is promising, but future work has to balance compression performance against deployability, for instance through adaptive compression.