Transformer Based Entropy Model

Transformer-based entropy models are revolutionizing data compression, particularly for images and time series, by leveraging their superior ability to capture long-range dependencies within data distributions. Current research focuses on improving efficiency and speed through architectural innovations like grouped attention mechanisms, content-aware masking, and optimized context modeling within transformer networks, aiming to reduce computational complexity while maintaining or improving compression ratios. These advancements are leading to state-of-the-art performance in various applications, including image and video compression, and offer significant potential for improving the efficiency of data storage and transmission across diverse fields.