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.

Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-server (or cloud). Data compression is applied to the intermediate tensor from the DNN that needs to be transmitted, addressing the challenge of optimizing the rate-accuracy-complexity trade-off. Existing split-computing approaches adopt ML-based data compression, but require that the parameters of either the entire DNN model, or a significant portion of it, be retrained for different compression levels. This incurs a high computational and storage burden: training a full DNN model from scratch is computationally demanding, maintaining multiple copies of the DNN parameters increases storage requirements, and switching the full set of weights during inference increases memory bandwidth. In this paper, we present an approach that addresses all these challenges. It involves the systematic design and training of bottleneck units - simple, low-cost neural networks - that can be inserted at the point of split. Our approach is remarkably lightweight, both during training and inference, highly effective and achieves excellent rate-distortion performance at a small fraction of the compute and storage overhead compared to existing methods.

As visual analytics continues to rapidly grow, there is a critical need to improve the end-to-end efficiency of visual processing in edge/cloud systems. In this paper, we cover algorithms, systems and optimizations in three major areas for edge/cloud visual processing: (1) addressing storage and retrieval efficiency of visual data and meta-data by employing and optimizing visual data management systems, (2) addressing compute efficiency of visual analytics by taking advantage of co-optimization between the compression and analytics domains and (3) addressing networking (bandwidth) efficiency of visual data compression by tailoring it based on analytics tasks. We describe techniques in each of the above areas and measure its efficacy on state-of-the-art platforms (Intel Xeon), workloads and datasets. Our results show that we can achieve >10X improvements in each area based on novel algorithms, systems, and co-design optimizations. We also outline future research directions based on our findings which outline areas of further performance and efficiency advantages in end-to-end visual analytics.

Human pose-estimation in a multi-person image involves detection of various body parts and grouping them into individual person clusters. While the former task is challenging due to mutual occlusions, the combinatorial complexity of the latter task is very high. We propose a greedy part assignment algorithm that exploits the inherent structure of the human body to lower the complexity of the graphical model, compared to any of the prior published works. This is accomplished by (i) reducing the number of part-candidates using the estimated number of people in the image, (ii) doing a greedy sequential assignment of partclasses, following the kinematic chain from head to ankle (iii) doing a greedy assignment of parts in each part-class set, to person-clusters (iv) limiting the candidate person clusters to the most proximal clusters using human anthropometric data and (v) using only a specific subset of pre-assigned parts for establishing pairwise structural constraints. We show that, these steps sparsify the bodyparts relationship graph and reduces the algorithm's complexity to be linear in the number of candidates of any single part-class. We also propose a method for spawning person-clusters from any unassigned significant body part to make the algorithm robust to occlusions. We show that, our proposed part-assignment algorithm, despite using a sub-optimal pre-trained DNN model, achieves state of the art results on both MPII and WAF pose datasets, demonstrating the robustness of our approach.