Video Dropouts and the Challenges they Pose to Video Quality Assessment

The media industry is rapidly adopting file-based workflows in all stages of the content lifecycle including transcoding, repurposing, delivery, etc. Additional complexities could be introduced during media transformations, which if not handled properly, could lead to issues in video perceived by the end consumer.The issues are due to errors caused by media capturing devices, encoding/transcoding devices, editing operations, pre- or post-processing operations, etc. A significant majority of video issues nowadays 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. Detection of such video quality (VQ) issues in the form of dropouts are gaining importance in the workflow quality checking and monitoring space, where the goal is to ensure content integrity, conformance to encoding standards, meta-data fields and most importantly, the perceived quality of the video that is ultimately delivered. This end video quality can certainly be measured and verified using manual checking processes, as was traditionally the case. However, such manual monitoring can be tedious, inconsistent, subjective, and difficult to scale in a media farm.
Automated video quality detection methods are gaining traction……..
This paper discusses various kinds of video dropouts, the source of these errors, and the challenges encountered in detection of these errors.
While adoption of file-based workflows provided more flexibility with the basic paradigm of file processing, it has also added complexities during media transformations. Improper handling of these complexities can lead to perceived video quality issues for the end consumer. The issues are due to errors caused by media capturing devices, encoding/transcoding devices, editing operations, pre- or post-processing operations, etc. A significant majority of video issues nowadays 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. Detection of such video quality (VQ) issues in the form of dropouts are gaining importance in the workflow quality checking and monitoring space, where the goal is to ensure content integrity, conformance to encoding standards, meta-data fields and most importantly, the perceived quality of the video that is ultimately delivered. This end video quality can certainly be measured and verified using manual checking processes, as was traditionally the case. However, such manual monitoring can be tedious, inconsistent, subjective, and difficult to scale in a media farm.
Automated video quality detection methods are gaining traction over manual inspection as these are more accurate, offer greater consistency, have the ability to handle large amount of video data without loss of accuracy and moreover, can be upgraded easily with changing parameters and standardizations. However, automatic detection of video dropouts is complex and a subject of ongoing research. The source where the artefacts are introduced has a bearing on the way the artefact manifests itself. Automatic detection of the variety of manifestations of video dropouts requires complex algorithmic techniques and is at the heart of a “good QC tool”. This paper discusses various kinds of video dropouts, the source of these errors, and the challenges encountered in detection of these errors.
You might also like...
Microphones: Part 11 - The State Of The Art… And The Potential Of MEMS Microphone Arrays
Here we look from the state of the art in microphones, to what the future may bring with the enticing theoretical potential of microphone arrays built using MEMS technology.
IP Monitoring & Diagnostics With Command Line Tools: Part 2 - Testing Remote Connections
In the previous article, we set the scene for working with the Command Line Interface (CLI) on a UNIX system. Now we will explore some techniques for performing basic tests on our network infrastructure to check for potential problems.
Microphones: Part 10 - Mid-Side (M-S) Recording And Processing
M-S techniques provide useful sound-field positioning and a convenient way to check mono compatibility. We explain the hard science behind this often misunderstood technique.
Microphones: Part 9 - The Science Of Stereo Capture & Reproduction
Here we look at the science of using a matched pair of microphones positioned as a coincident pair to capture stereo sound images.
Microphones: Part 8 - Audio Vectorscopes
The audio vectorscope is an excellent tool for assuring quality in stereo sound production, because it makes the virtual sound image visible in the same way that a television vectorscope allows the color signals to be seen.