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With the rapid expansion of image data and advancements in artificial intelligence, a significant portion of image analysis is performed by machines rather than humans. To enhance efficiency in data transmission and visual analysis, on-demand transmission becomes a preferable approach, which adaptively transmits the necessary information based on specific requirements. In this paper, we propose a novel joint feature and image compression scheme to facilitate flexible on-demand transmission. The bitstreams generated by the proposed scheme can be adapted to multiple machine vision tasks and image reconstruction based on specific needs. To achieve a good balance between the feature-based visual analysis performance and computational overhead at the receiver side, we adopt a reversible neural network as the feature extractor. The extracted features contain all information from the original image and necessitate a low-complexity analysis network. Additionally, we develop end-to-end compression models for multi-granularity features and image signals, where prediction models are incorporated in both feature/image space and latent space to improve the efficiency of joint compression. Furthermore, several feature transform blocks are designed to align the features with the requirements of different tasks. Experimental results on the COCO dataset show that the proposed compression method outperforms state-of-the-art image codecs on several machine vision tasks, and can also achieve comparable results in terms of image reconstruction.
Tu et al. (Tue,) studied this question.