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The escalating surge in data generation presents formidable challenges toinformation technology, necessitating advancements in storage, retrieval, andutilization. With the proliferation of artificial intelligence and big data,the "Data Age 2025" report forecasts an exponential increase in global dataproduction. The escalating data volumes raise concerns about efficient dataprocessing. The paper addresses the predicament of achieving a lowercompression ratio while maintaining or surpassing the compression performanceof state-of-the-art techniques.  This paper introduces a lossy compression framework grounded in theperceptron model for data prediction, striving for high compression quality.The contributions of this study encompass the introduction of positive andnegative factors within the relative-to-absolute domain transformationalgorithm, the utilization of a three-layer perceptron for improved predictiveaccuracy, and data selection rule modifications for parallelized compressionwithin compression blocks. Comparative experiments with SZ2.1's PW_REL modedemonstrate a maximum compression ratio reduction of 17.78%.  The article is structured as follows: the introduction highlights the dataexplosion challenge; related work delves into existing solutions; optimizationof mapping algorithms in the relative and absolute domains is expounded inSection 3,the design of the new compression framework is detailed in Section4,In Section 5 we describe the whole process and give pseudo-code, and inSection 6, our solution is evaluated. Finally, in Section 7, we provide anoutlook for future work.

Significant improvement of lossy compression rate and speed of HPC data using perceptron parallelized compression