Compression for machines is an emerging field, where inputs are encoded while
optimizing the performance of downstream automated analysis. In scalable coding
for humans and machines, the compressed representation used for machines is
further utilized to enable input reconstruction. Often performed by jointly
optimizing the compression scheme for both machine task and human perception,
this results in sub-optimal rate-distortion (RD) performance for the machine
side. We focus on the case of images, proposing to utilize the pre-existing
residual coding capabilities of video codecs such as VVC to create a scalable
codec from any image compression for machines (ICM) scheme. Using our approach
we improve an existing scalable codec to achieve superior RD performance on the
machine task, while remaining competitive for human perception. Moreover, our
approach can be trained post-hoc for any given ICM scheme, and without creating
a coupling between the quality of the machine analysis and human vision.

提出了一种利用视频编解码器中的残差编码能力创建可扩展编解码器的方法，这种方法能够提高现有的可扩展编解码器在机器任务方面的 RD 性能，同时在人类感知方面保持竞争性。

VVC+M: 人机插拔式可扩展图像编码

VVC+M: Plug and Play Scalable Image Coding for Humans and Machines

We present methods for conditional and residual coding in the context of
scalable coding for humans and machines. Our focus is on optimizing the
rate-distortion performance of the reconstruction task using the information
available in the computer vision task. We include an information analysis of
both approaches to provide baselines and also propose an entropy model suitable
for conditional coding with increased modelling capacity and similar
tractability as previous work. We apply these methods to image reconstruction,
using, in one instance, representations created for semantic segmentation on
the Cityscapes dataset, and in another instance, representations created for
object detection on the COCO dataset. In both experiments, we obtain similar
performance between the conditional and residual methods, with the resulting
rate-distortion curves contained within our baselines.

本文提出了一种在可扩展编码中应用条件编码和残差编码的方法，并在计算机视觉任务中优化重建任务的速率失真性能，获得了类似的结果。