As the production, the variety, and the consumption of born-digital video grows, so does the demand for acquiring, curating and preserving large-scale digital video collections. A multidisciplinary team of curators, computer scientists and video engineers we explore the use of Non-Reference Image and Video Quality Algorithms (I/VQA), specifically of BRISQUE in this paper, to automatically derive ranges of video quality. An important characteristic of these algorithms is that they are modeled to human perception. We run the algorithms in a High Performance Computing (HPC) environment to obtain results for many videos at the same time, accelerating time to results and precision in computing per-frame and per-video quality assessment scores. Results, which were evaluated quantitatively and qualitatively, suggest that BRISQUE identifies the distortions in which it was trained, and performs well in videos that have natural scenes and do not have drastic scene changes. While we found that this particular model is not apt for evaluating collections with varied content, the results suggest that research into other I/VQA models is promising, and that their implementation at large scale can narrow the problem of curating very digital video collections and lead to preservation and access decisions based on informed priorities.