A Path Toward Bayer Compression for Automotive Applications

Intelligent Transportation systems require sensor data to function safely. Cameras present increased resolution and dynamic range, demanding significant datarates which existing vehicular wired and wireless communication networks cannot support. We here propose to use video compression to reduce the required datarate. This aspect must be carefully analysed, as any manipulation of sensor data can have implications on vehicle safety. Most of the widely adopted compression schemes consume demosaiced RGB or YCbCr (Luma, blue difference, red difference) data, which increases the data amount without adding information. RGB data has been traditionally generated for human perception and is potentially not essential for automotive. To leverage existing and highly efficient compression schemes, this paper focuses on studying and proposing novel Bayer adaptation techniques to make Bayer directly consumable by traditional codecs. Two novel and computationally light Colour Space Transform (CST) Bayer adaptation techniques, namely Average CST and De-Interlacing CST, are proposed and compared to state-of-the-art techniques. Adapted Bayer data are deployed into widely usedcompression standards, such as H.264 and JPEG codecs, and then evaluated with IQA (Image Quality Assessment) metrics and deep neural network-based perception, particularly object detection. Through experiments, these novel techniques provide the best perception performance at around 700-1300 kb/fr for H.264 and at over 1000 kb/fr for JPEG, with a negligible reduction of the colour accuracy. This work sets a foundation for further research to optimise Bayer compression for automotive applications. The outcomes of this research will be beneficial to optimise data transmissions and communications for intelligent and cooperative transportation systems.