  Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the
potential to radically change the way we travel. Many such vehicles currently
rely on segmentation and object detection algorithms to detect and track
objects around its surrounding. The data collected from the vehicles are often
sent to cloud servers to facilitate continual/life-long learning of these
algorithms. Considering the bandwidth constraints, the data is compressed
before sending it to servers, where it is typically decompressed for training
and analysis. In this work, we propose the use of a learning-based compression
Codec to reduce the overhead in latency incurred for the decompression
operation in the standard pipeline. We demonstrate that the learned compressed
representation can also be used to perform tasks like semantic segmentation in
addition to decompression to obtain the images. We experimentally validate the
proposed pipeline on the Cityscapes dataset, where we achieve a compression
factor up to $66 \times$ while preserving the information required to perform
segmentation with a dice coefficient of $0.84$ as compared to $0.88$ achieved
using decompressed images while reducing the overall compute by $11\%$.
