# Weighted Moving Averages

Everyone would like to see FFmpeg performance graphs based on data collected from FATE. One day, I got down to analyzing the millisecond runtime data for one configuration regarding one of the longer tests in the suite. The numbers were all over the map and directly graphing them would prove confusing and meaningless.

Then I had an idea which draws on my limited experience with digital signal processing as it pertains to multimedia. What about plotting points that represent the average of point n along with, say, points (n-15)..(n+15)? Then I refined the idea a bit– how about multiplying each point by a factor such that point n gets the most consideration while points further away from n receive progressively less consideration?

And then I quickly covered up all evidence of the brainstorm for fear of catching a beatdown from a tough gang of statisticians for devising the most idiotic idea in the history of statistics. Imagine my surprise when I was recently reading up on a completely different topic and found the exact same techniques described in practical application. It turns out they even have formal names: Moving averages, in which various forms of weighted moving averages comprise a sub-category.

This is not the first time I have re-invented something that I would later learn is called a “wheel” (the first time I can recall was when I independently figured out a bubble sort algorithm). I’m sure a spreadsheet program can be coerced to massage the data in this manner, but I decided to go the Python route; I have to use Python to effectively extract the data from MySQL into a usable form anyway.

I created a Python script that takes a list of FATE configuration IDs and a test spec number. For each configuration ID, gather the history of millisecond run times for the specified test. Run a weighted moving average using the current run time and the 9 run times prior. The current run time is multiplied by 10, (n-1) is multiplied by 9, down through (n-9) which is multiplied by 1. Divide the average by 55 (10+9+…+1). For a more elegant mathematical explanation, see the Wikipedia entry. This script allows me to dump all the performance data for a series of configurations like, for example, all of the PowerPC configurations that run on the same machine.

Then, we turn the data over to OpenOffice Calc for graphing… <sigh…>

Dear OpenOffice.org: I hate you so much. Sometimes you elect to plot data on a graph in a sane range and sometimes you opt to begin from 0. The latter is shown above, where the former would have been much more appropriate. As it stands, any useful data to be gathered from the visualizing the trend of the weighted moving averages is lost. And don’t even get me started (again) on your fragility. Or your atrocious software update mechanism.

Anyway, the above graph shows performance over time for the idroq-video-encode test run on various PowerPC configurations. The graph actually does reveal at least one oddity– the orange pulse wave that represents gcc 4.1.2’s performance over time. That might be worth looking into, particularly since it’s on the high part of the wave right now.

Below is the same thing, only with data collected from running h264-conformance-mv1_brcm_d which is the most computationally intensive H.264 conformance sample in the FATE suite. And it looks like I mislabeled ‘gcc 4.3.3’ as ‘gcc 4.3.2’ in both graphs. Oh well; not going through the pain of regenerating those graphs with OpenOffice now.

What to do about performance graphs going forward? I really don’t think it would be worthwhile to be able to pull graphs from the database relating to performance data over time for arbitrary tests. Most of the tests are simply too short to yield any useful trends. This would be better suited to the idea of running certain tasks less frequently. Various configurations should be made to run longevity tests on full movies encoded in various formats, once every day or 2. CPU runtime performance data (in contrast to wall clock time data) should be collected on those runs and graphed for analysis.

BTW, if anyone knows of better desktop graphing apps, do let me know. Any system, I guess. As long as it can read a CSV file and create a competent graph in a reasonable amount of time on a 2 GHz CPU, I should be able to tolerate it.

# FFmpeg Perceptual Audio Test Plan

There have been some problems with FATE audio testing. First off, the qt-ima4-stereo test spec was testing against the wrong file for the past year. Stereo IMA ADPCM decoding could have broken in QuickTime and we might have never been alerted. Sloppy.

More seriously, I found out that many of my existing, bitexact audio tests have not been constructed properly. This is due to the fact that these 2 commands:

```ffmpeg -i file.ext file.wav
ffmpeg -i file.ext -f wav - > file.wav
```

do not yield equivalent sets of bytes inside file.wav. Part of the reason is that, after writing out all the audio samples, the muxer needs to rewind to the header so that it can write the data payload length. When writing data to stdout, the program does not have the option to rewind the output stream. However, I don’t understand the entire discrepancy. Using the file qt-ima4-mono with the above command lines:

```1156652 surge.wav
1146924 surge-stdout.wav
```

The file that is routed through stdout is notably smaller (9728 bytes smaller). I was going to write this off as the stdout file failing to be flushed. However, the behavior is consistent across all machines and platforms.

My proposed solution is to update all of the audio tests to use this raw format target:

```ffmpeg -i file.ext -f s16le -
```

Since the output is equivalent to:

```ffmpeg -i file.ext -f s16le file.s16le

1156608 surge.s16le
1156608 surge-stdout.s16le
```

Moving right along, there is the much bigger task of testing perceptual audio decoders. Working down the FATE Test Coverage list, these perceptual audio codecs will get the naive, one-off wave reference treatment in lieu of a proper conformance suite: ATRAC3, RealAudio Cooker, DCA (DTS), IMC, Nellymoser, Qcelp, QDesign, RealAudio 28.8, Truespeech, Vorbis, and WMA v1.

Then there is the matter of MPEG audio codecs for which we have access to extensive conformance suites. Thanks to Kostya and Benjamin for furnishing pointers to precise information discussing how to verify if your MP1/2/3 or AAC audio decoder is up to snuff. This page at Underbit describes exactly how the spec describes conformance for MPEG 1, layers 1, 2, and 3, and also evaluates the conformance of various implementations. The comparison ostensibly predates FFmpeg. This Mp4-tech mailing list post shows the way regarding AAC conformance.

So I need to automate the MP1/2/3 and AAC test entries. I estimate the automated process will work something like this:

• Decode encoded file
• Run comparison of decoded wave against original wave
• For MP1/2/3, this seems to entail converting both the FFmpeg output and the original wave output floating point numbers to a normalized range of -1.0..1.0, computing the root mean square of the difference signal, and verifying that the RMS is less than 1 / (32768 * sqrt(12))
• For AAC, well, I’m still researching the precise criteria
• If the decoded wave is within tolerance, add a new test

The part where I get a bit fuzzy is: what should the test spec be? Should I generate a reference wave and test future decoded waves against it using my one-off wave reference method? Or, should I just go ahead and compute the RMS of the difference signal? I assume that if I use the nifty numpy library for the task, it couldn’t possibly make any measurable difference in the performance of FATE testing vs. using the one-off wave reference method (computing absolute value of the difference signal and checking that no discrete points exceed 1).

One trade-off is that I would need to store the full 24-bit reference waves in order to properly compute RMS, which is 50% more data than I would need with the one-off method. And I’m still not sure how to process the 24-bit data in any event.