As image recognition models become more prevalent, scalable coding methods for machines and humans gain more importance. Applications of image recognition models include traffic monitoring and farm management. In these use cases, the scalable coding method proves effective because the tasks require occasional image checking by humans. Existing image compression methods for humans and machines meet these requirements to some extent. However, these compression methods are effective solely for specific image recognition models. We propose a learning-based scalable image coding method for humans and machines that is compatible with numerous image recognition models. We combine an image compression model for machines with a compression model, providing additional information to facilitate image decoding for humans. The features in these compression models are fused using a feature fusion network to achieve efficient image compression. Our method's additional information compression model is adjusted to reduce the number of parameters by enabling combinations of features of different sizes in the feature fusion network. Our approach confirms that the feature fusion network efficiently combines image compression models while reducing the number of parameters. Furthermore, we demonstrate the effectiveness of the proposed scalable coding method by evaluating the image compression performance in terms of decoded image quality and bitrate.

翻译：随着图像识别模型的日益普及，面向机器与人类的可扩展编码方法变得愈发重要。图像识别模型的应用场景包括交通监控和农场管理。在这些场景中，由于任务需要人类偶尔进行图像核查，可扩展编码方法展现出显著效果。现有的人机图像压缩方法虽能在一定程度上满足这些需求，但这些压缩方法仅针对特定图像识别模型有效。我们提出一种基于学习的可扩展图像编码方法，该方法兼容多种图像识别模型。通过将面向机器的图像压缩模型与提供额外信息以辅助人类图像解码的压缩模型相结合，利用特征融合网络融合这些压缩模型中的特征，从而实现高效图像压缩。本方法对额外信息压缩模型进行调整，通过使特征融合网络能够组合不同尺寸的特征来减少参数量。实验证实，特征融合网络能在降低参数量的同时高效整合图像压缩模型。此外，我们通过评估解码图像质量与比特率，验证了所提可扩展编码方法在图像压缩性能上的有效性。