Distributed Semantic Segmentation With Improved Rate-Distortion Trade-Off

Researchers propose two novel source codecs for distributed semantic segmentation that enable extremely low bitrates while improving rate-distortion performance, achieving state-of-the-art results at below 0.2 bits per pixel on ADE20K and 0.03 bits per pixel on Cityscapes, measured by mean intersection-over-union.

arXiv:2608.28684v1 Announce Type: new Abstract: Distributed deep neural networks DNNs for dense perception tasks such as semantic segmentation execute an encoder DNN on edge devices, and a decoder DNN typically on a large-scale cloud platform with a particular constraint on transmission bitrate. Recent works employ source codecs to enable bitrate-efficient transmission between the edge device and the cloud. However, as these approaches are typically bound to a particular type of source codec and alternative network architectures are often not explored, this results in a suboptimal rate-distortion RD trade-off in the low-bitrate regime. In this work, we propose two novel source codecs that \textit{enable extremely low bitrates, while improving RD performance}. We demonstrate the effectiveness of our proposed source codecs by achieving state-of-the-art performance in distributed semantic segmentation at below 0.2 0.03 bits per pixel, measured using the mean intersection-over-union metric on ADE20K Cityscapes .