Abstract: Image compression is a critical technology designed to minimize information redundancy during transmission while preserving the quality of the compressed image. With advancements in computer vision， images are increasingly consumed by machines in addition to humans， necessitating compression methods that cater to both human and machine vision requirements. While current learning-based image coding techniques have significantly improved human visual perception， they struggle to balance signal fidelity and semantic fidelity， limiting their ability to meet the needs of both audiences effectively. To address this limitation， this study proposes a task-adaptive， feature-decoupling scalable compression method. This approach supports multiple machine vision tasks using a single bitstream and enables selective or complete image reconstruction depending on specific requirements. The proposed method decouples image features into object and background features， compressing and reconstructing them independently. Reconstructed object features are employed for tasks such as object detection and semantic segmentation， whereas the fully reconstructed image caters to human visual perception. This method enhances compression efficiency by eliminating the need to reconstruct the entire image for visual tasks， thereby meeting the distinct demands of human perception. Furthermore， to address performance imbalances arising from variations in the importance of target regions， a plug-and-play task-adaptive unit is integrated into the target feature decoder. This unit enables task-specific adjustments to improve the analysis performance of reconstructed target images without requiring retraining of the entire network. Experimental results demonstrate that the proposed method outperforms conventional encoders and decoders in task performance while achieving superior Rate-Distortion efficiency. These findings underscore the potential of this method to advance scalable image compression for both human and machine vision applications.