Image Compression

Image compression aims to reduce the size of digital images while preserving acceptable quality, balancing data storage and transmission efficiency with visual fidelity. Current research heavily focuses on learned image compression (LIC), employing neural networks like autoencoders, transformers, and diffusion models to achieve superior rate-distortion performance compared to traditional methods. Key areas of investigation include improving the efficiency of these models, particularly for resource-constrained devices, and developing techniques to better preserve semantically important image features for downstream tasks like machine learning. Advances in LIC have significant implications for various fields, including satellite imagery, medical imaging, and web applications, by enabling efficient storage, transmission, and processing of large image datasets.