The exponential growth of multimedia applications and Internet of Things (IoT) systems has significantly increased the demand for efficient real-time data transmission and storage. Conventional lossless compression algorithms often suffer from limited adaptability, higher latency, and inefficient performance when handling heterogeneous multimedia streams such as images, audio, and sensor-generated data. This paper proposes an Adaptive Real-Time Lossless Compression Framework (ARLCF) that dyn amically selects compression parameters based on local statistical characteristics of incoming multimedia data. The proposed method integrates adaptive predictive encoding, dynamic Golomb-Rice parameter estimation, and lightweight entropy coding to achieve improved compression efficiency while maintaining low computational overhead. Experimental analysis demonstrates that the proposed framework achieves higher compression ratios, lower latency, and lower memory utilisation than traditional Huffman, LZW, and standard Golomb-Rice techniques. The framework is particularly suitable for edge devices, smart surveillance systems, healthcare monitoring, and industrial multimedia applications where real-time performance and data integrity are critical.
G et al. (Fri,) studied this question.