Region of interest enabled learned image coding for machines

Image and video coding for machines has been recently gaining more and more interest from both the industry and the research community. One successful approach is based on end-to-end (E2E) learned compression and has shown significant gains over the state-of-the-art conventional image coding methods. However, one of the remaining challenges for such E2E-learned image codecs for machines is to adaptively allocate the bits over different regions of the image, while retaining the machine vision performance. In this paper, we propose a method that leverages Regions-Of-Interest (ROIs) for bitrate allocation within a Learned Image Codec (LIC) for machines. In particular, the proposed method reduces the bits allocated for the background regions of the image by reducing the variance of the elements corresponding to the background regions in the latent representation. This results in more heavily quantized background areas, while keeping the quality of the ROI areas suitable for machine tasks. The proposed method achieves significant gains, -15.80% and -22.43% Pareto BD-rate reduction, over the baseline LIC on object detection and instance segmentation tasks, respectively. To the best of our knowledge, this is the first research paper proposing an ROI-based inference-time technology for Learned Image Coding for machines.