Semantic Compression

Semantic compression aims to reduce data size while preserving essential meaning, rather than focusing solely on pixel-perfect reconstruction. Current research emphasizes developing models that leverage semantic understanding, often employing autoencoders, transformers, and large language models (LLMs) to achieve this compression, particularly for images, videos, and text. This approach is significant because it enables efficient storage and transmission of data for various applications, including medical imaging, video analysis, and large language model inference, while maintaining sufficient semantic information for downstream tasks. The development of new benchmarks and metrics for evaluating semantic fidelity is also a key area of focus.