Automated video quality assessment methods have generally been based on measurements of engineering parameters such as ground sampling distance, level of blur, and noise. However, humans rate video quality using specific criteria that measure the interpretability of the video by determining the kinds of objects and activities that might be detected in the video. Given the improvements in tracking, automatic target detection, and activity characterization that have occurred in video science, it is worth considering whether new automated video assessment methods might be developed by imitating the logical steps taken by humans in evaluating scene content. This article will outline a new procedure for automatically evaluating video quality based on automated object and activity recognition, and demonstrate the method for several ground-based and maritime examples. The detection and measurement of in-scene targets makes it possible to assess video quality without relying on source metadata. A methodology is given for comparing automated assessment with human assessment. For the human assessment, objective video quality ratings can be obtained through a menu-driven, crowd-sourced scheme of video tagging, in which human participants tag objects such as vehicles and people on film clips. The size, clarity, and level of detail of features present on the tagged targets are compared directly with the Video National Image Interpretability Rating Scale (VNIIRS).