Video Dropouts and the Challenges they Pose to Video Quality Assessment

Video Dropouts and the Challenges they Pose to Video Quality Assessment File-based media QC workflows increasingly span a variety of native and transformed content. The added complexities due to media transformations such as transcoding, file delivery and editing lead to greater challenges for content video quality (VQ) monitoring. Many VQ issues are due to the loss or alteration in coded or uncoded video information, resulting in the distortion of the spatial and/or temporal characteristics of the video. These distortions in turn manifest themselves as video artefacts, termed hereafter as video dropouts. While the end VQ can be measured and verified using manual checking processes, this type of monitoring can be tedious, inconsistent, subjective and difficult to scale in a media farm. Automatic detection of video dropouts is the subject of intense ongoing research. It requires complex algorithmic techniques which are at the heart of an "effective QC tool". This background paper discusses various kinds of video dropouts, the source of these errors, and the challenges encountered in detection of these errors. Interra Systems' Baton is the leading file-based QC tool on the market today. Baton supports the detection of a large variety of video dropouts. The detection algorithms in Baton deploy appropriately selected and patented advanced image processing and computer vision techniques.

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