Learning-based Image Coding for Machine Intelligence with Variable-Rate

Image Coding for Machines (ICM) has yielded significant developments recently. Variable-rate support is necessary for image coding, while performance gap still exists, in learning-based image coding, between the single-model and multiple-fixed-models methods. This paper proposes a Machine Intelligence Variable-Rate Codec (MIVRCodec) with single-model method. We introduce a method to generate, compress, and utilize image semantic feature information, enabling the codec to adaptively process different semantic content of the image. Additionally, current studies employ fixed methods to remove redundant information between luminance and chrominance components, neglecting the dynamic characteristics of this redundancy and leading to its inappropriate utilization. We further propose a Color Dynamic Fusion Module (CDFM), which adaptively fuses image color component features based on various conditions (e.g., bitrate and image content) to utilize the redundancy among image color components as appropriately as possible. Lastly, we propose a Progressive Training Strategy (PTS) for training MIVRCodec. These proposed methods not only reduce performance loss in variable-rate ICM but also improve baseline performance. Experimental results demonstrate that our proposed MIVRCodec works well in the bitrate range corresponding to meaningful accuracy intervals in machine intelligence tasks using a single model, achieving coding efficiency on par with multiple fixed-rate models and surpassing existing state-of-the-art codecs.