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Video Coding for Machines (VCM) is committed to bridging to an extentseparate research tracks of video/image compression and feature compression,and attempts to optimize compactness and efficiency jointly from a unifiedperspective of high accuracy machine vision and full fidelity human vision. Inthis paper, we summarize VCM methodology and philosophy based on existingacademia and industrial efforts. The development of VCM follows a generalrate-distortion optimization, and the categorization of key modules ortechniques is established. From previous works, it is demonstrated that,although existing works attempt to reveal the nature of scalable representationin bits when dealing with machine and human vision tasks, there remains a rarestudy in the generality of low bit rate representation, and accordingly how tosupport a variety of visual analytic tasks. Therefore, we investigate a novelvisual information compression for the analytics taxonomy problem to strengthenthe capability of compact visual representations extracted from multiple tasksfor visual analytics. A new perspective of task relationships versuscompression is revisited. By keeping in mind the transferability amongdifferent machine vision tasks (e.g. high-level semantic and mid-levelgeometry-related), we aim to support multiple tasks jointly at low bit rates.In particular, to narrow the dimensionality gap between neural networkgenerated features extracted from pixels and a variety of machine visionfeatures/labels (e.g. scene class, segmentation labels), a codebook hyperprioris designed to compress the neural network-generated features. As demonstratedin our experiments, this new hyperprior model is expected to improve featurecompression efficiency by estimating the signal entropy more accurately, whichenables further investigation of the granularity of abstracting compactfeatures among different tasks.

Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics