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：随着图像识别模型日益普及，面向机器与人类的可扩展编码方法的重要性日益凸显。图像识别模型的应用场景包括交通监控与农场管理等。在这些用例中，可扩展编码方法被证明是有效的，因为相关任务需要人类偶尔进行图像检查。现有面向人类与机器的图像压缩方法在一定程度上满足了这些需求。然而，这些压缩方法仅对特定图像识别模型有效。我们提出一种基于学习的、面向人类与机器的可扩展图像编码方法，该方法兼容多种图像识别模型。我们将面向机器的图像压缩模型与提供辅助信息的压缩模型相结合，以促进人类对图像的解码。通过特征融合网络对这两个压缩模型中的特征进行融合，以实现高效的图像压缩。我们通过允许特征融合网络中不同尺寸特征的组合，调整辅助信息压缩模型以减少参数量。我们的方法证实了特征融合网络在有效结合图像压缩模型的同时能够减少参数量。此外，我们通过评估解码图像质量与比特率方面的图像压缩性能，证明了所提出的可扩展编码方法的有效性。