Scalable Image Coding for Humans and Machines Using Feature Fusion Network
This addresses the need for flexible image compression in applications like traffic monitoring and farm management where both automated analysis and occasional human checks are required, though it is incremental as it builds on existing compression methods.
The paper tackles the problem of scalable image coding that works for both human viewing and multiple machine recognition models, proposing a feature fusion network that efficiently combines compression models while reducing parameters, achieving improved decoded image quality and bitrate performance.
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.