  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.
