web: www.yuvsoft.com
e-mail: customers@yuvsoft.com
phones: +1 408 426 5988
+7 906 744 0865
X264 CODEC PARAMETERS COMPARISON
Methodology
Averaging Methods and Explanation of Charts
One of the most important characteristics of a codec is quality of
encoded video. Besides problems regarding how to measure
“video quality”, there are difficulties in comparing different codecs
or modes of functioning of a certain codec since it is non-trivial to
represent quality by a single value. Some reasonable assumptions
and well-grounded aggregation methods are necessary to perform
such a comparison. The following approach was used.
First of all, we run all chosen presets of x264 for all test
sequences at 10 different bitrates: 100, 225, 340, 460, 700, 938,
1140, 1340, 1840 and 2340 Kbps. Encoded sequences were
compared with corresponding originals using objective metrics
such as PSNR, SSIM, etc. It made us able to create and operate
with Bitrate/Quality charts or Rate-Distortion curves of the codec.
These data are necessary to correctly compare different modes
(presets) of the codec, or, as it also might be the case, to correctly
compare different codecs. We used the notion of “relative bitrate”
meaning what bitrate in percents should be to achieve the same
quality (by, for example, PSNR criterion) as for some reference
preset whose bitrate is taken for 100%.
The first step to get relative bitrate of two presets (codecs) is
“rotating” of Rate-Distortion (RD) charts, changing axis of the
charts (Figure 1, Figure 2). It allows us to calculate ratio of bitrates
for the same quality. The advantage of bitrates ratio for the same
quality instead of, for example, PSNR difference for the same
bitrate, is that bitrates ratio does not generally depend on an
objective quality metric being used.
After that it is necessary to choose interval of averaging. We used
an internal area of RD curves where missed bitrate values can be
interpolates between the nearest values (see Figure 2). It means
that we did not use extrapolation because of big possible mistakes
of RD curves extrapolation. Linear interpolation was used to get
values between the existing points. Previous experiments
convinced us that more complex interpolation methods usually
give very little for better accuracy.
To get average values we calculated sizes of areas under the
curves and divided one by the other (see Figure 3).
