Compression of Preprocessed Automotive Radar Data by Using Context-Adaptive Binary Arithmetic Coding

The increasing use of high-resolution radars in vehicles with central processing has led to a substantial rise in data within sensor networks. To cope with this challenge, researchers have explored radar data compression techniques aiming to minimize information loss, conserve resources, and enhance processing efficiency. These techniques involve lossy preprocessing, quantization, and the creation of a data stream. This paper focuses on three novel lossless compression methods for the data stream, leveraging Context-Adaptive Binary Arithmetic Coding (CABAC). The first method employs Exponential-Golomb coding, the second optimizes the preprocessed radar data stream, and the third isolates non-zero values and their corresponding indices within the data stream. Subsequently, these novel methods utilize CABAC and are proposed and compared against the original CABAC. The achieved compression ratios are evaluated and analyzed.