Friday 21 March 2025
The pursuit of efficient data storage and retrieval has long been a challenge for scientists and researchers, particularly in fields where massive datasets are generated at an alarming rate. To tackle this issue, a team of researchers has developed a novel approach to progressive lossy compression, dubbed IPComp.
At its core, IPComp is designed to compress scientific data while maintaining the flexibility to retrieve specific parts of the dataset with varying levels of fidelity. This allows users to access coarse approximations of their data quickly and then incrementally refine these approximations to higher fidelity as needed. The solution leverages a combination of interpolation-based prediction, multi-level bitplanes, and predictive coding techniques.
IPComp’s compression algorithm is based on an interpolation model that predicts the value of each pixel in the dataset using neighboring pixels. This approach enables the compressor to identify patterns and redundancies in the data, allowing for more efficient storage. The algorithm also employs multi-level bitplanes, which divide the dataset into smaller regions and encode each region using a different set of bits. This allows for greater control over the compression ratio and fidelity.
To further improve performance, IPComp incorporates predictive coding techniques that take advantage of the correlation between neighboring pixels in the dataset. By predicting the values of missing or corrupted pixels, the algorithm can reduce the amount of data required to represent the entire dataset.
The team evaluated IPComp on six real-world scientific datasets from various domains, including climate modeling and fluid dynamics. The results showed that IPComp outperformed existing state-of-the-art solutions in terms of compression ratio, retrieval efficiency, and fidelity. In particular, the algorithm achieved up to 487% higher compression ratios compared to other progressive compressors.
One of the key advantages of IPComp is its flexibility in supporting arbitrary error bounds and bitrates for data retrieval. This allows users to tailor their compression settings to specific applications or use cases, providing greater control over the trade-off between storage efficiency and fidelity.
To demonstrate the practical implications of IPComp, the researchers used the algorithm to compress a dataset from the Coupled Model Intercomparison Project (CMIP), a large-scale international effort to understand climate change. The results showed that IPComp was able to reduce the dataset size by up to 83% while maintaining a high level of fidelity.
The development of IPComp represents an important step forward in the pursuit of efficient data storage and retrieval for scientific applications.
Cite this article: “Efficient Data Storage and Retrieval Through Progressive Lossy Compression with IPComp”, The Science Archive, 2025.
Data Compression, Scientific Datasets, Progressive Lossy Compression, Interpolation-Based Prediction, Multi-Level Bitplanes, Predictive Coding, Climate Modeling, Fluid Dynamics, Coupled Model Intercomparison Project, Cmip
