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.

我们提出了一种基于学习的可扩展图像编码方法，适用于多种图像识别模型。通过将机器的图像压缩模型与人类的压缩模型相结合，利用特征融合网络实现了高效的图像压缩，并且减少了参数的数量。通过评估图像压缩性能，我们证明了这种可扩展编码方法的有效性。