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In the Internet of Things (IoT) communications, visual data are frequently processed among intelligent devices using artificial intelligence algorithms, replacing humans for analysis and decision-making while only occasionally requiring human scrutiny. However, due to high redundancy of compressive encoders, existing image coding solutions for machine vision are inefficient at runtime. To balance the rate-accuracy performance and efficiency of image compression for machine vision while attaining high-quality reconstructed images for human vision, this paper introduces a novel slimmable multi-task compression framework for human and machine vision in visual IoT applications. Firstly, image compression for human and machine vision under the constraint of bandwidth, latency, and computational resources is modeled as a multi-task optimization problem. Secondly, slimmable encoders are employed for multiple human and machine vision tasks in which the parameters of the sub-encoder for machine vision tasks are shared among all tasks and jointly learned. Thirdly, to solve the feature match between latent representation and intermediate features of deep vision networks, feature transformation networks are introduced as decoders of machine vision feature compression. Finally, the proposed framework is successfully applied to human and machine vision tasks’ scenarios, e.g., object detection and image reconstruction. Experimental results show that the proposed method outperforms baselines and other image compression approaches on machine vision tasks with higher efficiency (shorter latency) in two vision tasks’ scenarios while retaining comparable quality on image reconstruction. |
