The escalating surge in data generation presents formidable challenges to information technology, necessitating advancements in storage, retrieval, and utilization. With the proliferation of artificial intelligence and big data, the "Data Age 2025" report forecasts an exponential increase in global data production. The escalating data volumes raise concerns about efficient data processing. The paper addresses the predicament of achieving a lower compression ratio while maintaining or surpassing the compression performance of state-of-the-art techniques. This paper introduces a lossy compression framework grounded in the perceptron model for data prediction, striving for high compression quality. The contributions of this study encompass the introduction of positive and negative factors within the relative-to-absolute domain transformation algorithm, the utilization of a three-layer perceptron for improved predictive accuracy, and data selection rule modifications for parallelized compression within compression blocks. Comparative experiments with SZ2.1's PW_REL mode demonstrate a maximum compression ratio reduction of 17.78%. The article is structured as follows: the introduction highlights the data explosion challenge; related work delves into existing solutions; optimization of mapping algorithms in the relative and absolute domains is expounded in Section 3,the design of the new compression framework is detailed in Section 4,In Section 5 we describe the whole process and give pseudo-code, and in Section 6, our solution is evaluated. Finally, in Section 7, we provide an outlook for future work.

翻译：数据生成量的急剧攀升对信息技术构成严峻挑战，要求存储、检索及利用技术同步革新。随着人工智能与大数据的普及，《数据时代2025》报告预测全球数据产量将呈指数级增长。海量数据对高效处理提出了更高要求。本文针对在保持或超越现有最优技术压缩性能的同时实现更低压缩率这一难题展开研究。提出一种基于感知器模型进行数据预测的有损压缩框架，追求高压缩质量。本研究的主要贡献包括：在相对-绝对域变换算法中引入正负因子、采用三层感知器提升预测精度、以及通过修改数据选择规则实现压缩块内并行化压缩。与SZ2.1的PW_REL模式对比实验显示，压缩率最大可降低17.78%。本文结构如下：引言阐述数据爆炸挑战；相关研究部分探讨现有方案；第3节详述相对域与绝对域映射算法优化；第4节介绍新压缩框架设计；第5节描述完整流程并给出伪代码；第6节评估解决方案；第7节展望未来工作方向。