  Recent years have seen a tremendous growth in both the capability and
popularity of automatic machine analysis of images and video. As a result, a
growing need for efficient compression methods optimized for machine vision,
rather than human vision, has emerged. To meet this growing demand, several
methods have been developed for image and video coding for machines.
Unfortunately, while there is a substantial body of knowledge regarding
rate-distortion theory for human vision, the same cannot be said of machine
analysis. In this paper, we extend the current rate-distortion theory for
machines, providing insight into important design considerations of
machine-vision codecs. We then utilize this newfound understanding to improve
several methods for learnable image coding for machines. Our proposed methods
achieve state-of-the-art rate-distortion performance on several computer vision
tasks such as classification, instance segmentation, and object detection.
