Beyond compression: Revisiting the encoder for multi-task learning in AI-based image compression

This paper proposes a multi-task framework for learning-based image compression in which multiple tasks share a common latent representation while preserving compatibility with a single frozen reconstruction decoder. Unlike existing approaches that retrain both encoder and decoder for each task configuration, the proposed method adapts only the encoder and task-specific heads, maintaining decoder standardization and interoperability. Built upon the HiFiC codec, the framework supports additional tasks such as image super-resolution and facial feature extraction from the compressed domain. An adaptive multi-task loss balances compression efficiency and task performance. Experiments at different bitrates demonstrate that heterogeneous tasks can be integrated within a shared latent space while preserving competitive rate-distortion performance. These results support the development of interoperable AI-based compression systems for both visual reconstruction and downstream inference, under a fixed, shared decoder.