  Latency-critical computer vision systems, such as autonomous driving or drone
control, require fast image or video compression when offloading neural network
inference to a remote computer. To ensure low latency on a near-sensor edge
device, we propose the use of lightweight encoders with constant bitrate and
pruned encoding configurations, namely, ASTC and JPEG XS. Pruning introduces
significant distortion which we show can be recovered by retraining the neural
network with compressed data after decompression. Such an approach does not
modify the network architecture or require coding format modifications. By
retraining with compressed datasets, we reduced the classification accuracy and
segmentation mean intersection over union (mIoU) degradation due to ASTC
compression to 4.9-5.0 percentage points (pp) and 4.4-4.0 pp, respectively.
With the same method, the mIoU lost due to JPEG XS compression at the main
profile was restored to 2.7-2.3 pp. In terms of encoding speed, our ASTC
encoder implementation is 2.3x faster than JPEG. Even though the JPEG XS
reference encoder requires optimizations to reach low latency, we showed that
disabling significance flag coding saves 22-23% of encoding time at the cost of
0.4-0.3 mIoU after retraining.
