FaSDiff: Balancing Perception and Semantics in Face Compression via Stable Diffusion Priors

The introduction of FaSDiff marks a notable advancement in facial image compression technology, particularly in addressing the challenges associated with low bit rates that often lead to degraded image quality. By leveraging a stable diffusion prior, FaSDiff enhances both visual fidelity and semantic consistency, which are crucial for applications that rely on accurate facial representations. The framework incorporates a high-frequency-sensitive compressor to capture fine details and a low-frequency enhancement module to preserve semantic structures. Extensive experiments have demonstrated that FaSDiff outperforms existing state-of-the-art methods, making it a significant contribution to the field of computer vision and image processing. As facial image data continues to be deployed across various applications, the ability to compress this data efficiently while maintaining quality is increasingly important, highlighting the relevance of FaSDiff in contemporary technological contexts.