Efficient compression of correlated data is essential to minimize
communication overload in multi-sensor networks. In such networks, each sensor
independently compresses the data and transmits them to a central node due to
limited communication bandwidth. A decoder at the central node decompresses and
passes the data to a pre-trained machine learning-based task to generate the
final output. Thus, it is important to compress the features that are relevant
to the task. Additionally, the final performance depends heavily on the total
available bandwidth. In practice, it is common to encounter varying
availability in bandwidth, and higher bandwidth results in better performance
of the task. We design a novel distributed compression framework composed of
independent encoders and a joint decoder, which we call neural distributed
principal component analysis (NDPCA). NDPCA flexibly compresses data from
multiple sources to any available bandwidth with a single model, reducing
computing and storage overhead. NDPCA achieves this by learning low-rank task
representations and efficiently distributing bandwidth among sensors, thus
providing a graceful trade-off between performance and bandwidth. Experiments
show that NDPCA improves the success rate of multi-view robotic arm
manipulation by 9% and the accuracy of object detection tasks on satellite
imagery by 14% compared to an autoencoder with uniform bandwidth allocation.

该研究利用名为 NDPCA 的分布式压缩框架，将来自多个传感器的数据有效地压缩到任何可用带宽，以提高多视图机械臂操作和卫星图像目标检测成功率。

动态带宽下的任务感知分布式源编码

Task-aware Distributed Source Coding under Dynamic Bandwidth

Beyond achieving higher compression efficiency over classical image
compression codecs, deep image compression is expected to be improved with
additional side information, e.g., another image from a different perspective
of the same scene. To better utilize the side information under the distributed
compression scenario, the existing method (Ayzik and Avidan 2020) only
implements patch matching at the image domain to solve the parallax problem
caused by the difference in viewing points. However, the patch matching at the
image domain is not robust to the variance of scale, shape, and illumination
caused by the different viewing angles, and can not make full use of the rich
texture information of the side information image. To resolve this issue, we
propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes
side information at the decoder of the distributed image compression model.
Specifically, MSFDPM consists of a side information feature extractor, a
multi-scale feature domain patch matching module, and a multi-scale feature
fusion network. Furthermore, we reuse inter-patch correlation from the shallow
layer to accelerate the patch matching of the deep layer. Finally, we nd that
our patch matching in a multi-scale feature domain further improves compression
rate by about 20% compared with the patch matching method at image domain
(Ayzik and Avidan 2020).

采用多尺度特征域补丁匹配（MSFDPM）模块来充分利用分布式图像压缩模型中的附加信息，从而比图像域中的补丁匹配方法（Ayzik 和 Avidan 2020）更好地解决了视差问题，并提高了约 20% 的压缩率。