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Beyond Compression: Revisiting the Encoder for Multi-Task Learning in AI-Based Image Compression
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AI
DOI：10.1007/s11760-026-05366-7.png)
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Abstract
En 中文
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.
Keywords:
Learning-based image compression
Multi-task learning
Rate-distortion
Auto-encoders
Journal
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