## 2 Related Work
### Data Compression Methods  
The advent of the information age has led to explosive growth in data, posing significant challenges for data storage, transmission, and analysis, thus driving the research and application of data compression algorithms [3]. Data compression can be divided into lossless compression and lossy compression. Lossless compression retains all data without loss but typically achieves lower compression ratios, whereas lossy compression sacrifices some data accuracy to achieve higher compression ratios. In optimizing data compression algorithms, striking a balance between compression ratio and reconstruction loss is a key issue.  
Lossless compression typically employs statistical models and dictionary models. Statistical-model-based compression encoding involves coding data based on statistical data characteristics. Common algorithms include arithmetic coding [10] and Huffman coding [11], with Huffman coding holding a legendary status in the field of computing and often being used as a component in compression algorithms, such as in the classic JPEG compression [12]. On the other hand, dictionary-model-based compression encoding utilizes data redundancy, representing duplicate segments with shorter tags to achieve compression. LZ series algorithms, such as LZW and LZ77 [13], are representatives of dictionary-model-based compression algorithms, and well-known compression tools like WINZIP, WINRAR, and 7 ZIP benefit from LZ series algorithms.  
Traditional lossy compression can be categorized into prediction-based and transform-based methods. Differential pulse code modulation (DPCM) achieves compression by encoding the difference between the previous and current values of a signal for prediction. Meanwhile, classic JPEG compression achieves compression through discrete cosine transform (DCT), followed by quantization and entropy coding. However, in the field of scientific data compression, the precision loss caused by lossy compression can have adverse effects on subsequent computational analysis tasks. To address this, researchers have proposed error-controlled lossy compression, where the difference between the original data and reconstructed data is limited by a specified absolute error threshold during lossy compression. SZ [1,14] is a typical prediction-based error-bounded lossy compression model. For data that can be accurately predicted, it replaces them with the best curve-fitting model. For unpredictable data, it optimizes lossy compression through binary representation analysis. On the other hand, ZFP [15] is a typical representative of transform-based compression model design, including four key steps: segmenting data into fixed-size blocks, converting values into a universal fixed-point format, applying orthogonal block transforms to correlate data values, and embedding encoding.  
In recent years, with the development of deep learning technology, people have developed neural network models for fields such as computer vision and natural language processing. Autoencoders, with their excellent feature extraction capabilities, have performed well in image compression, and there have also been some cases of using autoencoders for scientific data compression. [PERSON] et al. (2021) [16] were the first to comprehensively evaluate the use of autoencoders for lossy compression of scientific data and confirmed that properly tuned autoencoders outperform SZ and ZFP in scientific data compression. Building on this, [PERSON] et al. (2021) [1] developed a framework for an autoencoder based on the error bounds of the SZ model. They fine-tuned the block and latent sizes and optimized the compression quality of the model and the compression efficiency of the latent vectors.