Impairments in video adversely affect the performance of computer vision applications. This paper presents a comprehensive impairment video dataset and proposes an innovative video quality assessment (VQA) method to evaluate the video impairment levels. First, a dataset of 300 short videos (see www.cdvl.org) is developed with representative impairments: 19 types of individual impairments, and 35 types of combined impairments (see www.iaitusa.com for the specific types of impairments). Second, an innovative no-reference (iNR) metric vector is presented to assess the video impairment levels. In particular, the iNR metric vector is defined by five impairment scores: The blur impairment score, the small noise patches impairment score, the whole frame illumination impairment score, the partial frame illumination impairment score, and the temporal impairment score. The iNR metric vector thus is not only able to define a novel failure rate (FR) for each video to characterize the nature of the leading impairment, but it can also quantify the impact caused by other impairments in the same video.