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