Understanding the features and capabilities
of the Netflix system provides a useful means
for comparing later product and service offerings. This is shown in Table 1, which I’ll refer to
in the individual product sections.
We’ve covered Netflix’s core schema in our
own article ( go2sm.com/netflixpertititle). As a
result of its VMAF analysis, Netflix adjusts the
data rate of the clips in the individual encoding
ladders, which all per-title solutions also do.
Less common (and more valuable) is the ability
to change the number of rungs in the encoding
ladder and the resolution of those rungs. The
benefits of these capabilities will become clear
when we look at Brightcove’s technology.
Custom options provide the user with the ability to control the output to some degree. While I
don’t know the options in the Netflix system, I’ll
cover the options available for the other four in
each section below.
The next point, bitrate control, is essential to
understanding the difference between Capped
CRF and the other techniques. That is, all other
technologies use their per-title functionality to
suggest the ideal data rate for each file in the
encoding ladder. This frees the user to choose
the bitrate control technique that he favors,
whether constant bitrate (CBR) or constrained
variable bitrate encoding (VBR). With capped
CRF, the per-title technique is the bitrate control technique, which raises QoE concerns that
I’ll cover when I get to that section.
The last feature is a post-encode quality check.
With Netflix, objective quality metrics are critical to base system operation, with quality measurements dictating each configuration decision.
While quality measurements aren’t as integral
to the Brightcove per-title schema, you can set
post-encode quality checks, ensuring that all output files meet a specified quality level.
So the features in Table 1 help you understand
how each per-title encoding scheme works and
what it does. Now let’s look at how to assess how
well each technology performs.
To test each technology, I encoded 14 files
with very mixed content, from screencam- and
PowerPoint-based videos, to a range of animated movies, to a variety of real-world content, including low- and high-motion videos. As a baseline, I encoded each clip to the fixed encoding
ladder shown in Table 2. Then I ran each technology’s per-title encode function to build another content-specific encoding ladder. As part
of the per-title encoding run, I allowed each
technology to increase the data rate as much
as 50% for challenging files. Then I compared
the original and per-title output files using both
PSNR and VMAF metrics.
These comparisons were simple for capped
CRF and Cambria, since both ladders contained
the same number of streams at the same resolution. It was more complicated for Brightcove
because in almost all cases, there were fewer
output streams than inputs. As you’ll see, with
Brightcove, I used data rate as the guide, comparing the CAE files to the files in the original
ladder that had the same or lower data rate. In
essence, this compares the fixed ladder/CAE