A Real-Time Lossless Data Compression Framework Using Dynamic Parameter Selection

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@article{1718600,
      author = {Anuja M, Kousalya Devi S},
      title = {A Real-Time Lossless Data Compression Framework Using Dynamic Parameter Selection},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {8},
      pages = {494-498},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718600.pdf},
      abstract = {The increasing deployment of multimedia systems, wireless sensor networks, and Internet of Things (IoT) devices has generated substantial volumes of data that require efficient storage and transmission. Traditional lossless compression algorithms often employ fixed encoding parameters that fail to adapt to changing data characteristics, resulting in reduced compression efficiency and increased processing overhead. This paper presents a Real-Time Lossless Data Compression Framework based on Dynamic Parameter Selection (DPS). The proposed framework continuously analyzes local data statistics and automatically selects optimal compression parameters during runtime. The approach combines statistical analysis, adaptive parameter control, and lightweight lossless encoding to improve compression performance while maintaining low computational complexity.},
      keywords = {Lossless Data Compression, Dynamic Parameter Selection, Real-Time Systems, Multimedia Data, IoT, Edge Computing.},
      month = {February},
      doi = {https://doi.org/10.64388/IREV5I8-1718600}
  }