A New Approach for Bayer Adaption Techniques in Compression

Existing wired communication vehicle network technology lacks the bandwidth required to support the data rates produced by the sensor suite for assisted and automated driving functions in the next generation of intelligent vehicles. Video from image sensors demands a very high bandwidth and cameras are continually being developed giving better resolution, bit-depth (dynamic range) and frame rate. To overcome this challenge, compression is a potential solution to reduce the required bandwidth. As modern automotive cameras produce Bayer images, it might be more effective to compress Bayer images directly instead of red-green-blue (RGB) images compression, which is currently widely deployed in compression schemes. By using Bayer, we avoid to triple the memory storage and preserve data fidelity by bypassing demosaicing and other non-reversible processes in the Image Signal Processing (ISP) pipeline. Bayer adaption techniques indicate methods to convert Bayer data to be used in traditional compression pipelines. In this research, we propose two novel Colour Space Transform (CST) techniques and implement them in combination with the widely used H.264 codec. The presented experiments show that the novel CST techniques are superior to direct and Separation Bayer adaption techniques when paired with object detection, specifically when the bit rate is between ≈ 750 to 1250 kb/fr. These results can support the future adoption of Bayer data and compression in future vehicles, addressing the problem of unsustainable perception sensors' datarates.