A Hybrid Compression Method of Streamlines for Flow Visualization
Donghan Liu, Wenke Wang · 2021
Streamline is one of the most commonly used visualization methods to describe flow field data. With the increase of data scale, accurate storage of streamlines needs a lot of storage space. How to store streamlines efficiently is an urgent problem to be solved. Streamline compression is an effective solution, however, the existing method can be further improved. This paper proposes an improved fitting algorithm based on piecewise Bézier curves, which is combined with a lossless compression algorithm for hybrid compression and provides a comparison with the existing method. This paper uses the streamlines generated by vector field data to carry out several comparative experiments to demonstrate our approach's effectiveness. The results show that the proposed method can achieve a higher compression ratio and strictly control the error.