We investigate the use of automated Video Quality Assessment (VQA) algorithms to evaluate digital video collections. These algorithms are driven by well-defined natural scene statistics (NSS), which capture the behavior of natural distortion-free videos. Because human vision has adapted to these real-world statistics over the course of evolution, quality predictions delivered by these NSS-based VQA algorithms correlate well with human opinions of quality. In particular, we expect these algorithms to accurately predict quality on sizable and diverse video collections. To test this hypothesis, we gathered a testbed of video clips that represent a larger video art collection. Next, we conducted a human study in which users scored the quality of the clips. Enabled by the human study, we trained three VQA algorithms (Video BLIINDS, BRISQUE, and VIIDEO) using our testbed collection to assess a real-world digital video art collection from our university museum. Two of the algorithms provided good automatic predictions of the quality of the videos. These same algorithms also highlighted limitations that arise when assessing artistic collections. We present current research progress and discuss future directions for testbed and algorithm improvement. Our ongoing effort furthers the field of Computational Archival Science by applying computational models of human perception to video appraisal and preservation tasks.