  The high amount of sensors required for autonomous driving poses enormous
challenges on the capacity of automotive bus systems. There is a need to
understand tradeoffs between bitrate and perception performance. In this paper,
we compare the image compression standards JPEG, JPEG2000, and WebP to a modern
encoder/decoder image compression approach based on generative adversarial
networks (GANs). We evaluate both the pure compression performance using
typical metrics such as peak signal-to-noise ratio (PSNR), structural
similarity (SSIM) and others, but also the performance of a subsequent
perception function, namely a semantic segmentation (characterized by the mean
intersection over union (mIoU) measure). Not surprisingly, for all investigated
compression methods, a higher bitrate means better results in all investigated
quality metrics. Interestingly, however, we show that the semantic segmentation
mIoU of the GAN autoencoder in the highly relevant low-bitrate regime (at
0.0625 bit/pixel) is better by 3.9% absolute than JPEG2000, although the latter
still is considerably better in terms of PSNR (5.91 dB difference). This effect
can greatly be enlarged by training the semantic segmentation model with images
originating from the decoder, so that the mIoU using the segmentation model
trained by GAN reconstructions exceeds the use of the model trained with
original images by almost 20% absolute. We conclude that distributed perception
in future autonomous driving will most probably not provide a solution to the
automotive bus capacity bottleneck by using standard compression schemes such
as JPEG2000, but requires modern coding approaches, with the GAN
encoder/decoder method being a promising candidate.
