Universal Adaptive Stream-Based Entropy Coding

Data stream is a major data type in modern information equipment such as sensory and video/sound devices, which is fast and continuously generated without any stall cycle. Due to artificial intelligence becoming a common tool to extend application domain for information equipment, reduction of streaming data via communication path is an emerged and crucial theme targeted to support fast and large data migration among edge devices and cloud servers for IoT applications. This paper proposes a novel lossless compression algorithm based on digram coding scheme called universal Adaptive Stream-based Entropy (ASE) coding. It handles data stream with an instant entropy calculation according to table occupation ratio and provides a universal compression mechanism by matching multiple data units in data stream. It organizes a compression pipeline of multiple ASE coding modules that scans a data stream and performs encoding from multiple data units into at least a single bit of a compressed data without pipeline stall. Universal ASE coding is suitable for hardware implementation with configurable pipeline depth and resource size of the comp/decompression module. This paper describes organization of the pipeline and the control mechanisms of the comp/decompression. The evaluation reveals effective compression performances according to the effective compression mechanism and optimization techniques.