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，面向机器与人类兼容的可扩展编码方法变得更加重要。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有效性。现有的人机兼容图像压缩方法在一定程度上满足了这些需求，但这些压缩方法仅对特定图像识别模型有效。本文提出一种基于学习的可扩展图像编码方法，能够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以促进人类图像解码的压缩模型相结合，通过特征融合网络实现两类压缩模型特征的融合，从而达成高效图像压缩。所提方法的辅助信息压缩模型通过允许特征融合网络中不同尺度特征的组合来减少参数量。实验证明，特征融合网络能在有效降低参数量的同时高效融合图像压缩模型。此外，我们通过解码图像质量和码率两项指标评估图像压缩性能，验证了所提可扩展编码方法的有效性。