Last time, I began a multi-part post to discuss techniques used in measuring video quality.  I introduced the concept of a Mean Opinion Score (MOS), which has been used for years as a measure of voice quality, and is now being used in a similar way to quantify the quality of a video stream.

Today, I'll continue this discussion by talking about two alternative methods for measuring video quality - "full-reference" and "reference-free" measurement technologies.  My hope is that this information may help you if you find yourself evaluating different video quality measurement solutions from different vendors.

If you imagine your user experience as you watch a stream of IP video arriving at your laptop or on your mobile device, your mind probably pictures something that is not perfect - the video probably pauses to buffer sometimes, the image itself might sometimes freeze or appear "blocky" or pixelated.  This is often the nature of today's video as it streams through bandwidth-constrained networks. 

In this environment, vendors are developing automated systems to analyze the video and provide some measurement of the video quality.  But they are choosing to do it in different ways.

Some vendors are developing solutions using what they call a "full reference" measurement model.  This means that they have a "reference" copy of the video - a "golden" copy, if you will - that existed before the video got compressed, sent through the network, and decompressed.  These vendors choose to measure the video quality by comparing the output from the network against the "reference" copy... in this way, the system can identify the areas of the image that have changed. 

 

This approach delivers excellent results, but is computationally expensive.  Sometimes, each pixel of each image must be compared to get a true result, and these calculations can often not be completed in real time.  And not all applications allow for a reference copy - user-generated content that is streamed from one iPhone to another does not allow for a comparison against a reference.

The most common application for full-reference video quality measurement is in pre-service network testing.  This is an extremely useful tool for verifying that video processing equipment can do its job, or that a network has been engineered to support the transport of high-quality video content.  These types of systems are generally deployed in lab environments, and can be used to measure the results from different network and equipment configurations.

An alternative approach is called "reference-free" measurement.  In this case, there is no "golden" copy of the content - instead, the video is analyzed dynamically for characteristics like blurriness or blockiness.  In fact, in some reference-free systems, the vendor actually measures network characteristics and then predicts the effects that these network conditions will cause on the video stream.

Reference-free solutions can typically complete their analysis in real-time with acceptable computational requirements.  The drawback is that their analysis is not as precise as that provided in a full-reference model.  For example, the original video may not have been very good - the reference free method will (accurately) claim that the video coming from the network has poor quality (and so the network might be suspect).  The full-reference implementation will tell you that the video coming out of the network is the same as what went in, and so the network and equipment are working properly.  Reference-free methods are typically deployed today for in-service video quality measurement systems that require real-time results.

These two methods have adopted different philosophies, and each is appropriate for different target applications.  Full-reference systems make great sense in pre-service lab testing, where precise quality measurement is important and real-time results are not necessary.  A reference-free measurement system is a better choice for in-service monitoring, since it can perform its job in real-time and give good information about the video quality.  The key is to identify the needs of your business, and choose the model that best fits your requirements.