# Data Compression

* :scroll: [Data Compression](data-compression.pdf)

   > This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano and Huffman in the 40's, to a technique developed in 1986.

## Scientific Data Compression

* :scroll: [Fast Error-bounded Lossy HPC Data Compression with SZ](https://www.mcs.anl.gov/papers/P5437-1115.pdf)

  > This is the first version of SZ. In this paper, SZ is introduced to achieve data reduction using regression-based data point prediction.

* :scroll: [Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization](https://arxiv.org/pdf/1706.03791.pdf)

  > This work is known as SZ-1.4. In this work, SZ employs multi-dimensional data prediction so that data with dimension larger than 1 is no longer linearized into single dimension before compression. In this way, more data locality is preserved thus compression ratio is improved.

* :scroll: [Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets](https://www.mcs.anl.gov/~hguo/publications/LiangDTLLGCC18.pdf)

  > This work is known as SZ-2.0. In this work, authors proposed an online selection tool between 2 predictors, the mean-integrated Lorenzo predictor and linear regression-based predictor. Users can choose the predictor that yields larger compression ratio with higher prediction accuracy.

 * :scroll: [Fixed-Rate Compressed Floating-Point Arrays](http://vis.cs.ucdavis.edu/vis2014papers/TVCG/papers/2674_20tvcg12-lindstrom-2346458.pdf)
 
* :scroll: [FPC: A High-Speed Compressor for Double-Precision Floating-Point Data](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.388.2782&rep=rep1&type=pdf)