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愈发凸显。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，由于任务需要人类偶尔进行图像核查，可扩展编码方法被证明具有显著优势。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，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、兼容多种图像识别模型的面向人类与机器的可扩展图像编码方法。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理解。通过特征融合网络对两个压缩模型中的特征进行融合，实现高效的图像压缩。本方法的辅助信息压缩模型经过优化调整，通过在特征融合网络中支持不同尺度特征的组合，有效减少了参数量。实验证实，特征融合网络在降低参数量的同时，能够高效整合图像压缩模型。此外，我们从解码图像质量与比特率两个维度评估图像压缩性能，验证了所提可扩展编码方法的有效性。