Recent years have seen a tremendous growth in both the capability and popularity of automatic machine analysis of media, especially images and video. As a result, a growing need for efficient compression methods optimised for machine vision, rather than human vision, has emerged. To meet this growing demand, significant developments have been made in 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 greatly extend the current rate-distortion theory for machines, providing insight into important design considerations of machine-vision codecs. We then utilise this newfound understanding to improve several methods for learned image coding for machines. Our proposed methods achieve state-of-the-art rate-distortion performance on several computer vision tasks - classification, instance and semantic segmentation, and object detection.

Thanks to advances in computer vision and AI, there has been a large growth in the demand for cloud-based visual analytics in which images captured by a low-powered edge device are transmitted to the cloud for analytics. Use of conventional codecs (JPEG, MPEG, HEVC, etc.) for compressing such data introduces artifacts that can seriously degrade the performance of the downstream analytic tasks. Split-DNN computing has emerged as a paradigm to address such usages, in which a DNN is partitioned into a client-side portion and a server side portion. Low-complexity neural networks called ‘bottleneck units' are introduced at the split point to transform the intermediate layer features into a lower-dimensional representation better suited for compression and transmission. Optimizing the pipeline for both compression and task-performance requires high-quality estimates of the information-theoretic rate of the intermediate features. Most works on compression for image analytics use heuristic approaches to estimate the rate, leading to suboptimal performance. We propose a high-quality ‘neural rateestimator’ to address this gap. We interpret the lower-dimensional bottleneck output as a latent representation of the intermediate feature and cast the rate-distortion optimization problem as one of training an equivalent variational auto-encoder with an appropriate loss function. We show that this leads to improved rate-distortion outcomes. We further show that replacing supervised loss terms (such as cross-entropy loss) by distillation-based losses in a teacher-student framework allows for unsupervised training of bottleneck units without the need for explicit training labels. This makes our method very attractive for real world deployments where access to labeled training data is difficult or expensive. We demonstrate that our method outperforms several state-of-the-art methods by obtaining improved task accuracy at lower bi-trates on image classification and semantic segmentation tasks.