Random thoughts

This page contains the latest entries thave I've published in my personal weblog. It discusses random bits related to my life, software development, my studies and such. The RSS for this feed is available from http://blogs.gnome.org/, as is the complete history.

Time for something new

In the beginning of December, Frederik was born. He’s growing up nicely.

At the end of December, I succesfully defended my PhD thesis (see earlier post) and was awarded a PhD for my research titled “Notch signaling in forebrain neurogenesis”. In January, the PhD was officially awarded.

So as my family expands and needs a bigger house, and my old way-to-spend-the-day came to an end, it was time for something new. Earlier this week, I started a new job as engineer at the big G. Rumor has it that I’ll be working on something related to video.

(Posted at Thu, 31 Mar 2011 04:25:30 +0000 - Comments)

Meet Frederik

The latest addition to our little sprouting family: Frederik Jie Bultje. Born December 12th, 2010 in New York.

Frederik Jie Bultje

(Posted at Mon, 13 Dec 2010 00:25:26 +0000 - Comments)

The world’s fastest VP8 decoder: FFmpeg

Performance chart for FFmpeg's VP8 decoder vs. libvpx

Jason does a great job explaining what we did and how we did it.

(Posted at Fri, 23 Jul 2010 23:07:39 +0000 - Comments)

Google’s VP8 video codec

Now that the hype is over, let’s talk the real deal. How good is Google’s VP8 video codec? Since “multiple independent implementations help a standard mature quicker and become more useful to its users”, me and others (David for the decoder core and PPC optimizations, Jason for x86 optimizations) decided that we should implement a native VP8 decoder in FFmpeg. This has several advantages from other approaches (e.g. linking to libvpx, which is Google’s decoder library for VP8):

• we can share code (and more importantly: optimizations) between FFmpeg’s VP8 decoder and decoders for previous versions of the VPx codec series (e.g. the entropy coder is highly similar compared to VP5/6). Thus, your phone’s future media player will be smaller and faster.
• since H.264 (the current industry standard video codec) and VP8 are highly similar, we can share code (and more importantly: optimizations) between FFmpeg’s H.264 and VP8 decoders (e.g. intra prediction). Thus, again, your desktop computer’s future media player will be smaller and faster.
• Since FFmpeg’s native VP3/Theora and Vorbis decoders (these are video/audio codecs praised by free software advocates) already perform better than the ones provided by Xiph (libvorbis/libtheora), it is highly likely that our native VP8 decoder will (once properly optimized) also perform better than Google’s libvpx. The pattern here is that since each libXYZ has to reinvent its own wheel, they’ll always fall short of reaching the top. FFmpeg comes closer simply because our existing wheels are like what you’d want on your next sports car.
• Making a video decoder is fun!

In short, we wrote a video decoder that heavily reuses existing components in FFmpeg, leading to a vp8.c file that is a mere 1400 lines of code (including whitespace, comments and headers) and another 450 for the DSP functions (the actual math backend of the codec, which will be heavily optimized using SIMD). And it provides binary-identical output compared to libvpx for all files in the vector testsuite. libvpx’ vp8/decoder/*.c plus vp8/common/*.c alone is over 10,000 lines of code (i.e. this excludes optimizations), with another > 1000 lines of code in vpx/, which is the public API to actually access the decoder.

Current work is ongoing to optimize the decoder to outperform libvpx on a variety of computer devices (think beyond your desktop, it will crunch anything; performance becomes much more relevant on phones and such devices). More on that later.

Google's Test Suite, Vector 15 screenshot

Things to notice so so far:

• Google’s VP8 specs are not always equally useful. They only describe the baseline profile (0). Other profiles (including those part of the vector testsuite, i.e. 1-3) use features not described in the specifications, such as chroma fullpixel motion vector (MV) rounding, a bilinear motion compensation (MC) filter (instead of a subpixel six-tap MC filter). Several parts of the spec are incomplete (“what if a MV points outside the frame?”) or confusing (the MV reading is oddly spread through 3 sections in a chapter, where the code in each section specifically calls code from the previous section, i.e. they really are one section), which means that in the end, it’s much quicker to just read libvpx source code rather than depend on the spec. Most importantly, the spec really is a straight copypaste of the decoder’s source code. As a specification, that’s not very useful or professional. We hope that over time, this will improve.
• Google’s libvpx is full of (hopefully) well-performing assembly code, quite some of which isn’t actually compiled or used (e.g. the PPC code), which makes some of us wonder what the purpose of its presence is.
• Now that VP8 is released, will Google release specifications for older (currently undocumented) media formats such as VP7?

(Posted at Sun, 27 Jun 2010 17:31:03 +0000 - Comments)

WMAVoice postfilter

I previously posted about my ongoing studies on the WMA Voice codec. A basic implementation of the actual codec was submitted and accepted/applied into FFmpeg SVN. Speech codecs work at ultra-low bitrates (~10kbps and lower) and suffer from obvious encoding artifacts, leading to “robotic” output sounds. Also, depending on the source (imaging a phone conversation in a mall), samples often have considerable levels of background noise. These types of artifacts are common to all speech codecs, and there are a variety of postfilters meant to reduce their effects. In fact, most speech codecs use the exact same filters. Imagine the smile on a developer’s face if a common proprietary postfilter can be implemented by calling no more than 3-4 already-implemented functions (as was the case with QCELP, another speech codec).

This was almost the case with WMAVoice, with one exception. This was the first time we saw an implementation of a Wiener filter. The purpose of the filter is noise reduction. Clearly, if noisy signal = signal + noise, then signal = noisy signal – noise. Sounds simple, right? The math is actually a little complex, but fortunately this is quite well-documented in the scientific literature of signal processing. The idea is that noise has lower signal strength than the intended signal. By increasing the contrast between the strength of these two, you decrease noise and thus enhance perception of the signal itself.

Here’s what the filter does:

• Take FFT (“frequency distribution”) of the LPCs (“time-independent representation of signal”);
• Calculate a power spectrum of these, which is basically a representation of the strongest power/frequency pairs versus the weakest ones, along with the desired level/strength of noise subtraction, as quasi-coefficients;
• turn these into actual denoising filter coefficients using a Hilbert/Laplace transform;
• apply these to the FFT of the “noisy” output of the speech synthesis filter.

The resulting patch was applied to SVN trunk last week. Thanks to Alex (hm, old…) and Vitor (hm, no blog…) for helping me understand! Time for something new, I guess…

(Posted at Fri, 30 Apr 2010 22:18:37 +0000 - Comments)