Image and Video Compression of Fluid Flow Data

Acquiring and analyzing high fidelity spatio-temporal data is crucial to many problems in fluid mechanics and this results in large data storage requirements. Thus far, modal analyses, sub-sampling and local re-simulation, autoencoders, and generative networks have been explored for data compression with some success but generally remain problem-specific. With explosive demand in the multimedia industry for data storage and sharing, advancements in image and video compression have accelerated with many algorithms producing negligible quality losses at substantial compression ratios. We explore the efficacy of spatial compression techniques such as JPEG and JPEG-2000, and spatio-temporal techniques such as H.264, H.265, and AV1 on various fluid flow data. These multimedia compression techniques are compared for examples of laminar cylinder wake flow, two-dimensional decaying homogenous isotropic turbulence, and three-dimensional turbulent channel flow. We observe that compressed flow fields with such techniques hold physical validity in terms of temporal correlations and kinetic energy distribution. The flexibility and scalability of these multimedia compression algorithms suggest an expansive potential within this field.