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• VP8 Codec Optimization Update

  16 juin 2010, par noreply@blogger.com (John Luther) — inside webm

  Since WebM launched in May, the team has been working hard to make the VP8 video codec faster. Our community members have contributed improvements, but there’s more work to be done in some interesting areas related to performance (more on those below).

  
  

  Encoder

  
  The VP8 encoder is ripe for speed optimizations. Scott LaVarnway’s efforts in writing an x86 assembly version of the quantizer will help in this goal significantly as the quantizer is called many times while the encoder makes decisions about how much detail from the image will be transmitted.

  For those of you eager to get involved, one piece of low-hanging fruit is writing a SIMD version of the ARNR temporal filtering code. Also, much of the assembly code only makes use of the SSE2 instruction set, and there surely are newer extensions that could be made use of. There are also redundant code removal and other general cleanup to be done ; (Yaowu Xu has submitted some changes for these).
  
  At a higher level, someone can explore some alternative motion search strategies in the encoder. Eventually the motion search can be decoupled entirely to allow motion fields to be calculated elsewhere (for example, on a graphics processor).
  

  Decoder

  
  Decoder optimizations can bring higher resolutions and smoother playback to less powerful hardware.

  Jeff Muizelaar has submitted some changes which combine the IDCT and summation with the predicted block into a single function, helping us avoid storing the intermediate result, thus reducing memory transfers and avoiding cache pollution. This changes the assembly code in a fundamental way, so we will need to sync the other platforms up or switch them to a generic C implementation and accept the performance regression. Johann Koenig is working on implementing this change for ARM processors, and we’ll merge these changes into the mainline soon.

  In addition, Tim Terriberry is attacking a different method of bounds checking on the "bool decoder." The bool decoder is performance-critical, as it is called several times for each bit in the input stream. The current code handles this check with a simple clamp in the innermost loops and a less-frequent copy into a circular buffer. This can be expensive at higher data rates. Tim’s patch removes the circular buffer, but uses a more complex clamp in the innermost loops. These inner loops have historically been troublesome on embedded platforms.
  
  To contribute in these efforts, I’ve started working on rewriting higher-level parts of the decoder. I believe there is an opportunity to improve performance by paying better attention to data locality and cache layout, and reducing memory bus traffic in general. Another area I plan to explore is improving utilization in the multi-threaded decoder by separating the bitstream decoding from the rest of the image reconstruction, using work units larger than a single macroblock, and not tying functionality to a specific thread. To get involved in these areas, subscribe to the codec-devel mailing list and provide feedback on the code as it’s written.
  

  Embedded Processors

  
  We want to optimize multiple platforms, not just desktops. Fritz Koenig has already started looking at the performance of VP8 on the Intel Atom platform. This platform need some attention as we wrote our current x86 assembly code with an out-of-order processor in mind. Since Atom is an in-order processor (much like the original Pentium), the instruction scheduling of all of the x86 assembly code needs to be reexamined. One option we’re looking at is scheduling the code for the Atom processor and seeing if that impacts the performance on other x86 platforms such as the Via C3 and AMD Geode. This is shaping up to be a lot of work, but doing it would provide us with an opportunity to tighten up our assembly code.
  
  These issues, along with wanting to make better use of the larger register file on x86_64, may reignite every assembly programmer’s (least ?) favorite debate : whether or not to use intrinsics. Yunqing Wang has been experimenting with this a bit, but initial results aren’t promising. If you have experience in dealing with a lot of assembly code across several similar-but-kinda-different platforms, these maintainability issues might be familiar to you. I hope you’ll share your thoughts and experiences on the codec-devel mailing list.
  
  Optimizing codecs is an iterative (some would say never-ending) process, so stay tuned for more posts on the progress we’re making, and by all means, start hacking yourself.
  
  It’s exciting to see that we’re starting to get substantial code contributions from developers outside of Google, and I look forward to more as WebM grows into a strong community effort.
  
  John Koleszar is a software engineer at Google.

  

• Easily track Events within Matomo Analytics thanks to Matomo Tag Manager

  7 juin 2019, par Matomo Core Team — Analytics Tips

  Easily track Events within Matomo Analytics thanks to Matomo Tag Manager

  Introduction

  In this article we’ll cover what events in Matomo Analytics are ; and how you can easily set them up thanks to Matomo Tag Manager.

  Key concepts within this article

  • Events
  • Quick definition of the Tag Management System
  • Matomo config
  • Creating triggers
  • Variables

  What are events in Matomo Analytics and why are they useful ?

  Events allow you to measure interactions on your website which are not defined by default. With them you can measure clicks on some elements of a page, such as, how visitors are interacting with dropdown menus, media players, scrolling behaviours etc. You can also use them to define goals which make them a key feature in Matomo Analytics. Learn more about tracking events in Matomo.

  You can easily access the Events report in Matomo Analytics by clicking on the Behaviour category :

  

  And you may read the following message and feel a bit frustrated :

  

  “There is no data for this report” is a nice way to say, “Hey, you are tracking just a tiny part of what Matomo Analytics can do for you.”

  Matomo is a great software, but it can’t guess what you want to track.

  In order for Matomo to register those event tracking interactions, you’ll need to explain it by adding a line of code similar to this one when the interaction happens :

   

  _paq.push(['trackEvent', 'Here you enter whatever you want', 'Here too', 'and here also']);

   

  Let’s imagine you want to track a click on an HTML button, your code will look something similar to this at the moment of the interaction :

  Click Me !

  As you can imagine, two main challenges will arise for non developers :

  1. How to access the source code ?
  2. How to define the interaction ?

  Luckily, Matomo Tag Manager makes those steps easy for you. Let’s see how the same tracking is implemented with Matomo Tag Manager.

  A quick definition of what Matomo Tag Manager is

  Matomo Tag Manager lets you manage all your tracking and marketing tags in one easy-to-access place. Please visit this page to learn more about Matomo Tag Manager. In order to start using it, you’ll need to copy/paste a tracking code, named a “container”, within the section of your pages.

  Once the container is on your website, all you need to do is to follow these simple steps :

  1. Add a Matomo Tag.
  2. Assign the condition to fire the tag (what we call the trigger).
  3. Publish your work.
  4. And enjoy

  1 – Add a Matomo Event tracking code

  All you have to do here is click on “CREATE NEW TAG”

  

  Once selected, just mention how you’d like this tag to be named (it is for your internal purpose here so always use an explicit tag name) and the Matomo configuration (the default configuration setting will be fine in most of the cases) :

  

  Then Matomo Tag Manager will ask you the type of tracking you’d like to deploy, in this case, this is an event so select “Event” :

  

  Then all you need is to indicate the values you’d like to push through your event :

  

  To put it in perspective, all we did here through Matomo Tag Manager was implement the equivalent of the following line of code :

  _paq.push(['trackEvent', 'Element interactions', 'Click button', 'Click Me']);

  Let’s now see how can we do this on click code part which we call a trigger.

  2 – Assign the condition to fire the tag

  In order to execute the event we’ll need to define when it will happen. You do this by clicking on Create a new trigger now :

  

  As the interaction we’d like to track is happening when a click occurs, it will be a click trigger, and as our button is not a link, we’ll select All Elements Click :

  

  Once selected you’ll need to be precise on the condition in which the event will be triggered. In this case we do not want to have events pushed every time someone is clicking on something on our website. We only want to track when they click on this specific button. That’s the reason why our trigger is set to fire only when the click occurs on an element which has an id and has the value “cta” :

  Once you click on the green button named CREATE NEW TRIGGER, you validate the tag by clicking on CREATE NEW TAG :

  

  

  Then you can move to the last part.

  3 – Publish your work

  Tag Managers are very powerful as they allow you to easily execute any JavaScript code, CSS or even text content on your websites.

  That’s why when you create your tag it doesn’t go live straight away on your website. In order to do this you need to publish your tag and this is what the “Publish” category is designed for :

  

  After that, click on the second button if you’re confident your tag and trigger are both ready to go live :

  

  4 – Enjoy

  Well done. As your tag is now live, every click made on this button will now be pushed to your Matomo Analytics account within :
  

  1. The visitor log report :

  

  2. The events report :

  

  You may now be asking, “That’s great, but can I collect something more exciting than clicks ?” 

  Of course you can ! This is what the Matomo Tag Manager is all about.

  As long as you can express it through a trigger you can really push whatever you want to Matomo Analytics. You can track scrolling patterns, an element visible on the page like an image, an ad or the time spent on the page. The options are now open to you with Tag Manager.

  Give them a try ! Change the triggers, start playing around with variables and discover that the possibilities are endless.

  Happy analytics,
  Matomo team

• Trimming and batch converting arbitrary video files to WebMs with python and ffmpeg

  22 juin 2018, par Romtromon

  I’ve got a whole lot of video files that I want to break up into multiple WebMs, each containing a trimmed portion of a specific video.

  I plan on having a csv file attached to each video file (having the same filename as the video) with a column structure similar to :

  Start Time | End Time | Rotation | Output Filename

  And I want to parse the csv files using python to execute ffmpeg, and I did a quick search and found the ffmpeg-python library and thought it might do the trick. The problem is, I don’t know the first thing about ffmpeg or video encoding. I’ve tried reading through the ffmpeg documentation and trying to replicate stuff using ffmpeg-python but the furthest I can get is an output video of the same format as the input file, trimmed (but mpv still shows the duration as the duration of the original file but cuts off playback when the end of the trim is reached) and without audio.

  As a side note, I currently use a software named "WebM for Retards" (excuse the offensive title) which uses ffmpeg and I’m happy with its output but it’s very tedious for my requirement. However I noticed that these are the arguments passed by using the software :

  -f nut -i pipe:0   -c:v libvpx -pix_fmt yuv420p -threads 8 -slices 4 -metadata title="This is a title" -ac 2 -c:a libvorbis -qmin 28 -crf 30 -qmax 32 -qcomp 1 -b:v 0 -qscale:a 3 -f webm -y "C:\Output.webm"

  I’d be happy if I could replicate output similar to what this provides. Thanks in advance for any help !