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<p>As the amount of global data is growing exponentially, lossy compression technology has become an inevitable trend in artificial intelligence and big data applications. However, existing compression algorithms are difficult to further improve the compression ratio while ensuring compression performance. Therefore, this paper proposes PSZ (Perceptron-based SZ), a data prediction compression framework based on a lightweight perceptron model. This work mainly includes three aspects of innovation: (1) introducing positive and negative factors to achieve effective conversion from relative domain to absolute domain and eliminate additional symbol storage; (2) building a lightweight four-layer perceptron network to improve prediction accuracy; (3) modifying the data selection rules of the prediction surface to achieve parallel compression within the compression block and improve the compression speed. In the comparative experiment with SZ2.1’s PW_REL, PSZ achieved a compression ratio improvement of up to 17.78% on multiple test sets.</p>

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Perceptron-based parallel compression method with high compression ratio under pointwise relative error bound

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • XinZhe Chen,
  • Weijing Qin,
  • Lin Qiao,
  • Jianjiang Li

摘要

As the amount of global data is growing exponentially, lossy compression technology has become an inevitable trend in artificial intelligence and big data applications. However, existing compression algorithms are difficult to further improve the compression ratio while ensuring compression performance. Therefore, this paper proposes PSZ (Perceptron-based SZ), a data prediction compression framework based on a lightweight perceptron model. This work mainly includes three aspects of innovation: (1) introducing positive and negative factors to achieve effective conversion from relative domain to absolute domain and eliminate additional symbol storage; (2) building a lightweight four-layer perceptron network to improve prediction accuracy; (3) modifying the data selection rules of the prediction surface to achieve parallel compression within the compression block and improve the compression speed. In the comparative experiment with SZ2.1’s PW_REL, PSZ achieved a compression ratio improvement of up to 17.78% on multiple test sets.