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.

 Assisted and automated driving (AAD) systems heavily rely on data collected from perception sensors, such as cameras. While prior research has explored the quality of camera data via traditional and well-established image quality assessment (IQA) metrics (e.g. PSNR, SSIM, BRISQUE) or have considered when noisy/degraded data affects perception algorithms (e.g. deep neural network (DNN) based object detection), there are no works that approach the holistic relationship between IQA and DNN performance. This work proposes that traditional IQA metrics, designed to evaluate digital image quality according to human visual perception, can help to predict the sensor data degradation level that perception algorithms can tolerate before performance deterioration occurs. Consequently, a correlation analysis was conducted between 17 selected IQA metrics (with and without reference) and DNN average precision. The evaluated data was increasingly compressed to generate degradation and artefacts. Notably, the experimental results show that several IQA metrics had a strong positive correlation (exceeding correlation scores of 0.7) with average precision, with IW-SSIM and DSS having very high correlation (> 0.9). Interestingly, the results show that re-training BRISQUE on compressed data causes an exceptionally high positive correlation (> 0.97), making it very suitable for predicting the performance of DNN object detectors. By effectively relating traditional image quality metrics to DNN performance, this research offers a series of significant tools to understand and predict perception degradation based on the quality of data, thus resulting in a significant impact on the development of automated driving systems.Â