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

XinZhe Chen

doi:10.1007/s10586-026-06342-z

2026-07-21

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DOI：10.1007/s10586-026-06342-z

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随着全球数据量呈指数级增长，有损压缩技术在人工智能和大数据应用中已成为必然趋势。然而，现有压缩算法难以在保证压缩性能的同时进一步提高压缩比。因此，本文提出PSZ（基于感知器的SZ），一种基于轻量级感知器模型的数据预测压缩框架。本文的主要创新点包括三个方面：（1）引入正负因子实现相对域到绝对域的有效转换并消除额外的符号存储；（2）构建轻量级四层感知器网络以提高预测精度；（3）修改预测面的数据选择规则，实现压缩块内的并行压缩并提高压缩速度。在与SZ2.1的PW_REL的对比实验中，PSZ在多个测试集上实现了最高17.78%的压缩比提升。

Keyword:

Perceptron

Predictive models

Data transfer

Parallelization

Data engineering

Data compression

期刊

Cluster Computing-The Journal of Networks Software Tools and Applications

IF:

4.1

论文数:

5.1K

被引数:

7.5K

机构

department of computer science and technology

学者数:

论文数: 29

被引数: 0

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