Sur d’autres sites (15305)

• Simulating MPEG1/2 transmission over a noisy channel [on hold]

  17 novembre 2015, par StepTNT

  The question may sound out of scope here but this is my last resource.

  I need to write a software that does :

  1. Get an uncompressed video from disk
  2. Compress it into MPEG-1 or MPEG-2 being able to change quantization matrix, GOP size and/or macroblock size for DCT/motion search
  3. Apply a repetition code to add redundancy
  4. Simulate transmission over a noisy channel with given error rate
  5. Reconstruct the original stream from the repetition code
  6. Decode the video and compare it with the original one by gathering stats like frame-by-frame difference, file size and stuff like that

  This should by done by a nice GUI to show the input and output videos, alongside their frame difference.

  Given what I need to do, I can write some requirements :

  • An encoder which allows me to change some of the parameters (needed for point 2)
  • A language that allows me to work at bit level (needed for points 3 and 5)
  • A language that allows me to build a nice GUI using a designer (GUI is not the core of the project so I can’t afford wasting time by writing one)

  So far my searches have led to mixed results that are not giving me enough resources to start.

  My first find was this MATLAB project which implements MPEG and has some parameters that can be tweaked (like quantization matrix and GOP pattern for example).
  The problem here is that I don’t know MATLAB at all, so I have no idea on how to link everything and build a GUI.

  So the next step was to move to JAVA, and I found a LOT of FFMPEG wrappers, but none seems to allow me to set the parameters that I need. My last try was with Xuggler but the Wiki is down and the documentation does not talk about what I need. Plus, JAVA doesn’t work at bit level so I’d have issues applying the repetition code.

  Failing with JAVA led me to C# and DirectShowNet, but the documentation is quite lacking and I don’t know how to start because I didn’t find anything related to setting the parameters that I need using Filters.

  The question now is : is there any language/framework/platform that allows me to do what I need without having to deal with pure C/C++ ?
  I’d expect a lot of stuff on this matter since we’re talking about well known codecs, still I’m having a hard time finding what I need.

• How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation

  13 mars 2023, par Erin — Analytics Tips

  If you struggle to connect the dots on your customer journeys, you are researching the correct solution. 

  Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.

  That said, each attribution model has inherent limitations, which make the selection process even harder.

  This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation. 

  Pros and Cons of Different Attribution Models 

  

  First Interaction 

  First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.

  Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU). 

  Pros 

  • Reflects the start of the customer journey
  • Shows channels that bring in the best-qualified leads 
  • Helps track brand awareness campaigns

  Cons 

  • Ignores the impact of later interactions at the middle and bottom of the funnel 
  • Doesn’t provide a full picture of users’ decision-making process 

  Last Interaction 

  Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels. 

  If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect. 

  Pros 

  • Reports bottom-of-the-funnel events
  • Requires minimal data and configurations 
  • Helps estimate cost-per-lead or cost-per-acquisition

  Cons 

  • No visibility into assisted conversions and prior visitor interactions 
  • Overemphasise the importance of the last channel (which can often be direct traffic) 

  Last Non-Direct Interaction 

  Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product. 

  Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion. 

  Pros 

  • Improved channel visibility, compared to Last-Touch 
  • Avoids over-valuing direct visits
  • Reports on lead-generation efforts

  Cons 

  • Doesn’t work for account-based marketing (ABM) 
  • Devalues the quality over quantity of leads 

  Linear Model

  Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.

  It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.

  Pros 

  • Focuses on all touch points associated with a conversion 
  • Reflects more steps in the customer journey 
  • Helps analyse longer sales cycles

  Cons 

  • Doesn’t accurately reflect the varying roles of each touchpoint 
  • Can dilute the credit if too many touchpoints are involved 

  Time Decay Model 

  Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).

  This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns. 

  Pros 

  • Helps track longer sales cycles and reports on each touchpoint involved 
  • Allows customising the half-life of decay to improve reporting 
  • Promotes conversion optimization at BoFu stages

  Cons 

  • Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
  • Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)

  Position-Based Model 

  Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches. 

  For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels. 

  Pros 

  • Helps establish the main channels for lead generation and conversion
  • Adds extra layers of visibility, compared to first- and last-touch attribution models 
  • Promotes budget allocation toward the most strategic touchpoints

  Cons 

  • Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
  • Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints

  How to Choose the Right Multi-Touch Attribution Model For Your Business 

  If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.

  To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability. 

  Marketing Objectives 

  Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities. 

  In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase. 

  When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales. 

  Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases. 

  Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….

  Sales Cycle Length 

  As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry. 

  Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months. 

  That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution. 

  Data Availability 

  Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data. 

  Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK. 

  Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting. 

  Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature. 

  When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts. 

  How to Implement Multi-Touch Attribution

  Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking). 

  Here’s a step-by-step walkthrough to help you get started. 

  Select a Multi-Touch Attribution Tool 

  The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.

  To make the right call prioritise five factors :

  • Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models. 
  • Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options. 
  • Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users. 
  • Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software. 
  • Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis. 

  Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations. 

  Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price). 

  Set Up Proper Data Collection 

  Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up : 

  • Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page. 
  • Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints. 
  • Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc. 

  Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.

  Configure Goals and Events 

  Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours. 

  For example : If your goal is lead generation, you can track :

  • Newsletter sign ups 
  • Product demo requests 
  • Gated content downloads 
  • Free trial account registration 
  • Contact form submission 
  • On-site call bookings 

  In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action. 

  To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc). 

  Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy. 

  Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner). 

  Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.

  Test and Validated the Selected Model 

  A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases. 

  For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that. 

  That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis. 

  Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group. 

  In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great. 

  The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results. 

  Conclusion

  A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI. 

  Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types. 

  As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.

  Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.

• Transcode WEBM to RTMP

  7 avril 2024, par Justin White

  Summary

  


  My goal is to take a webcam stream from the browser and feed it into a program called Restreamer that takes in an RTMP stream.

  


  I've deduced that the browser Recording API produces Blobs that can be saved as WEBM.

  


  In order to stream the WEBM content to Restreamer I am attempting to use FFmpeg. I've read that forcing FLV format is required but otherwise all of the arguments are Greek to me. I've been unable to find a comparable topic where someone has tried to go from WEBM to RTMP. I have found examples of going the other direction, but reversing the FFmpeg command proved unfruitful.

  


  Saving to FLV works fine. Using the following command, I am able to transcode a WEBM file to FLV and play it on VLC :
  
ffmpeg -i ~/big-buck-bunny_trailer.webm -f flv out.flv

  


  However, if instead of outputting to a file I pass it to RTMP I get the following output :
  
ffmpeg -i ~/Downloads/big-buck-bunny_trailer.webm -f flv "rtmp://example.com/live"
  

  


  ffmpeg version 5.0.1 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12 (GCC)
  configuration: --prefix=/usr --bindir=/usr/bin --datadir=/usr/share/ffmpeg --docdir=/usr/share/doc/ffmpeg --incdir=/usr/include/ffmpeg --libdir=/usr/lib64 --mandir=/usr/share/man --arch=x86_64 --optflags='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection' --extra-ldflags='-Wl,-z,relro -Wl,--as-needed -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 ' --extra-cflags=' -I/usr/include/rav1e' --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libvo-amrwbenc --enable-version3 --enable-bzlib --enable-chromaprint --disable-crystalhd --enable-fontconfig --enable-frei0r --enable-gcrypt --enable-gnutls --enable-ladspa --enable-libaom --enable-libdav1d --enable-libass --enable-libbluray --enable-libbs2b --enable-libcdio --enable-libdrm --enable-libjack --enable-libfreetype --enable-libfribidi --enable-libgsm --enable-libilbc --enable-libmp3lame --enable-libmysofa --enable-nvenc --enable-openal --enable-opencl --enable-opengl --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librav1e --enable-librtmp --enable-librubberband --enable-libsmbclient --enable-version3 --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libv4l2 --enable-libvidstab --enable-libvmaf --enable-version3 --enable-vapoursynth --enable-libvpx --enable-vulkan --enable-libglslang --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxvid --enable-libxml2 --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-avfilter --enable-libmodplug --enable-postproc --enable-pthreads --disable-static --enable-shared --enable-gpl --disable-debug --disable-stripping --shlibdir=/usr/lib64 --enable-lto --enable-libmfx --enable-runtime-cpudetect
  libavutil      57. 17.100 / 57. 17.100
  libavcodec     59. 18.100 / 59. 18.100
  libavformat    59. 16.100 / 59. 16.100
  libavdevice    59.  4.100 / 59.  4.100
  libavfilter     8. 24.100 /  8. 24.100
  libswscale      6.  4.100 /  6.  4.100
  libswresample   4.  3.100 /  4.  3.100
  libpostproc    56.  3.100 / 56.  3.100
Input #0, matroska,webm, from '/home/kyjus25/big-buck-bunny_trailer.webm':
  Metadata:
    encoder         : http://sourceforge.net/projects/yamka
    creation_time   : 2010-05-20T08:21:12.000000Z
  Duration: 00:00:32.48, start: 0.000000, bitrate: 533 kb/s
  Stream #0:0(eng): Video: vp8, yuv420p(progressive), 640x360, SAR 1:1 DAR 16:9, 25 fps, 25 tbr, 1k tbn (default)
  Stream #0:1(eng): Audio: vorbis, 44100 Hz, mono, fltp (default)
HandShake: client signature does not match!
Stream mapping:
  Stream #0:0 -> #0:0 (vp8 (native) -> flv1 (flv))
  Stream #0:1 -> #0:1 (vorbis (native) -> mp3 (libmp3lame))
Press [q] to stop, [?] for help
Output #0, flv, to 'rtmp://example.com/live':
  Metadata:
    encoder         : Lavf59.16.100
  Stream #0:0(eng): Video: flv1 ([2][0][0][0] / 0x0002), yuv420p(tv, bt470bg/unknown/unknown, progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 1k tbn (default)
    Metadata:
      encoder         : Lavc59.18.100 flv
    Side data:
      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: N/A
  Stream #0:1(eng): Audio: mp3 ([2][0][0][0] / 0x0002), 44100 Hz, mono, fltp (default)
    Metadata:
      encoder         : Lavc59.18.100 libmp3lame
WriteN, RTMP send error 32 (136 bytes)7kB time=00:00:00.39 bitrate= 136.7kbits/s speed=71.2x    
WriteN, RTMP send error 32 (35 bytes)
WriteN, RTMP send error 9 (42 bytes)
av_interleaved_write_frame(): Operation not permitted
    Last message repeated 1 times
[flv @ 0x55d0dd0af700] Failed to update header with correct duration.
[flv @ 0x55d0dd0af700] Failed to update header with correct filesize.
Error writing trailer of rtmp://example.com/live: Operation not permitted
frame=   53 fps=0.0 q=4.3 Lsize=     146kB time=00:00:02.45 bitrate= 486.8kbits/s speed=42.8x    
video:128kB audio:19kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
Error closing file rtmp://example.com/live: Operation not permitted
Conversion failed!


  


  There are several interesting rabbit holes to follow here, but after following all of them I've come up with nothing.

  


  • HandShake: client signature does not match! :
  
More of a warning than an error, I assumed because I was going from "WEBM -> FLV" instead of the more traditional "MP4 -> FLV".

  


  • av_interleaved_write_frame(): Operation not permitted :
  
I found several issues on this. One of them calling it a storage issue, the other calling it a file permissions issue. I have plenty of disk space and have tried setting the input file to 777 permissions. However, the examples I've found on it being a file permissions issue all deal with outputting to a file rather than to an an RTMP IP.

  


  • Failed to update header with correct duration :
  
Advice I found was to add -flvflags no_duration_filesize to the command, which does suppress both "Failed to update..." errors, but does not fix the over-arching issue.

  


  What I've Tried

  


  • Multi-format transcoding

  


  MP4 to RTMP does work correctly :
  
ffmpeg -i ~/Downloads/big-buck-bunny_trailer.mp4 -f flv "rtmp://example.com/live"
  
Theoretically, I could stream the WEBM to a file, transcode that to an MP4 file, and then transcode that to FLV/RTMP. Sounds awful.

  


  • Pay for a service (Wowza, Flashphoner, api.video, etc)

  


  Unfortunately precisely what I am trying to avoid.

  


  • WebRTC to RTMP ?

  


  WebRTC seems to be a peer-to-peer connection and doesn't play nicely with a server/client scenario.

  


  • WebRTC to other ingest formats

  


  Restreamer also supports incoming streams of HLS, DASH, RTP, RTSP, RTMP, and SRT. However, these all seem to be examples of network sources that would be exposed via an IP URL. I am not sure that FFmpeg can do that.

  


  • Utilizing ffmpeg-wasm instead of CLI

  


  Available here, I thought that maybe by using a browser implementation I may get different results. But no. Not even an error to the console.

  


  • Streaming from OBS

  


  For the record, yes, I have tried streaming from OBS instead of going through FFmpeg and the Restreamer platform itself does work for normal use. I use it often.

  


  Post Script

  


  I've not been able to find any relevant solutions online. I am shocked that streaming from a browser webcam has not been solved 1000 times prior. This is related to a question made 7 years ago but it was not resolved and Flash is no longer an option.

  


  Recommendations

  


  • Adding -c:v libx264 -flags:v +global_header -c:a aac -ac 2 :
  

  


  ffmpeg version 5.0.1 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12 (GCC)
  configuration: --prefix=/usr --bindir=/usr/bin --datadir=/usr/share/ffmpeg --docdir=/usr/share/doc/ffmpeg --incdir=/usr/include/ffmpeg --libdir=/usr/lib64 --mandir=/usr/share/man --arch=x86_64 --optflags='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection' --extra-ldflags='-Wl,-z,relro -Wl,--as-needed -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 ' --extra-cflags=' -I/usr/include/rav1e' --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libvo-amrwbenc --enable-version3 --enable-bzlib --enable-chromaprint --disable-crystalhd --enable-fontconfig --enable-frei0r --enable-gcrypt --enable-gnutls --enable-ladspa --enable-libaom --enable-libdav1d --enable-libass --enable-libbluray --enable-libbs2b --enable-libcdio --enable-libdrm --enable-libjack --enable-libfreetype --enable-libfribidi --enable-libgsm --enable-libilbc --enable-libmp3lame --enable-libmysofa --enable-nvenc --enable-openal --enable-opencl --enable-opengl --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librav1e --enable-librtmp --enable-librubberband --enable-libsmbclient --enable-version3 --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libv4l2 --enable-libvidstab --enable-libvmaf --enable-version3 --enable-vapoursynth --enable-libvpx --enable-vulkan --enable-libglslang --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxvid --enable-libxml2 --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-avfilter --enable-libmodplug --enable-postproc --enable-pthreads --disable-static --enable-shared --enable-gpl --disable-debug --disable-stripping --shlibdir=/usr/lib64 --enable-lto --enable-libmfx --enable-runtime-cpudetect
  libavutil      57. 17.100 / 57. 17.100
  libavcodec     59. 18.100 / 59. 18.100
  libavformat    59. 16.100 / 59. 16.100
  libavdevice    59.  4.100 / 59.  4.100
  libavfilter     8. 24.100 /  8. 24.100
  libswscale      6.  4.100 /  6.  4.100
  libswresample   4.  3.100 /  4.  3.100
  libpostproc    56.  3.100 / 56.  3.100
Input #0, matroska,webm, from '/home/kyjus25/big-buck-bunny_trailer.webm':
  Metadata:
    encoder         : http://sourceforge.net/projects/yamka
    creation_time   : 2010-05-20T08:21:12.000000Z
  Duration: 00:00:32.48, start: 0.000000, bitrate: 533 kb/s
  Stream #0:0(eng): Video: vp8, yuv420p(progressive), 640x360, SAR 1:1 DAR 16:9, 25 fps, 25 tbr, 1k tbn (default)
  Stream #0:1(eng): Audio: vorbis, 44100 Hz, mono, fltp (default)
HandShake: client signature does not match!
Stream mapping:
  Stream #0:0 -> #0:0 (vp8 (native) -> h264 (libx264))
  Stream #0:1 -> #0:1 (vorbis (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0x561564271fc0] using SAR=1/1
[libx264 @ 0x561564271fc0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x561564271fc0] profile High, level 3.0, 4:2:0, 8-bit
[libx264 @ 0x561564271fc0] 264 - core 163 r3060 5db6aa6 - H.264/MPEG-4 AVC codec - Copyleft 2003-2021 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=11 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, flv, to 'rtmp://example.com/live':
  Metadata:
    encoder         : Lavf59.16.100
  Stream #0:0(eng): Video: h264 ([7][0][0][0] / 0x0007), yuv420p(tv, bt470bg/unknown/unknown, progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 25 fps, 1k tbn (default)
    Metadata:
      encoder         : Lavc59.18.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
  Stream #0:1(eng): Audio: aac (LC) ([10][0][0][0] / 0x000A), 44100 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      encoder         : Lavc59.18.100 aac
Larger timestamp than 24-bit: 0xffffff77kB time=00:00:30.18 bitrate= 460.0kbits/s speed=19.7x    
[flv @ 0x56156425e440] Failed to update header with correct duration.
[flv @ 0x56156425e440] Failed to update header with correct filesize.
frame=  812 fps=475 q=-1.0 Lsize=    1901kB time=00:00:32.52 bitrate= 479.0kbits/s speed=  19x    
video:1354kB audio:508kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.123872%
[libx264 @ 0x561564271fc0] frame I:21    Avg QP:15.05  size:  8839
[libx264 @ 0x561564271fc0] frame P:293   Avg QP:20.78  size:  3087
[libx264 @ 0x561564271fc0] frame B:498   Avg QP:22.20  size:   593
[libx264 @ 0x561564271fc0] consecutive B-frames: 14.7%  7.6%  9.2% 68.5%
[libx264 @ 0x561564271fc0] mb I  I16..4: 42.6% 41.9% 15.5%
[libx264 @ 0x561564271fc0] mb P  I16..4:  4.0%  8.0%  0.9%  P16..4: 22.7%  8.0%  4.0%  0.0%  0.0%    skip:52.4%
[libx264 @ 0x561564271fc0] mb B  I16..4:  1.4%  1.5%  0.2%  B16..8: 12.0%  1.0%  0.2%  direct: 3.3%  skip:80.5%  L0:44.1% L1:47.1% BI: 8.8%
[libx264 @ 0x561564271fc0] 8x8 transform intra:53.6% inter:57.0%
[libx264 @ 0x561564271fc0] coded y,uvDC,uvAC intra: 35.1% 37.7% 11.2% inter: 7.8% 9.5% 2.4%
[libx264 @ 0x561564271fc0] i16 v,h,dc,p: 56% 21% 14%  9%
[libx264 @ 0x561564271fc0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 32% 26% 21%  3%  3%  4%  4%  3%  4%
[libx264 @ 0x561564271fc0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 27% 21% 18%  4%  7%  7%  6%  5%  4%
[libx264 @ 0x561564271fc0] i8c dc,h,v,p: 62% 21% 15%  2%
[libx264 @ 0x561564271fc0] Weighted P-Frames: Y:20.8% UV:19.5%
[libx264 @ 0x561564271fc0] ref P L0: 70.3% 17.7%  9.0%  2.9%  0.0%
[libx264 @ 0x561564271fc0] ref B L0: 89.3%  8.9%  1.9%
[libx264 @ 0x561564271fc0] ref B L1: 96.4%  3.6%
[libx264 @ 0x561564271fc0] kb/s:341.30
[aac @ 0x561564223140] Qavg: 952.636


  


  Seems to complete successfully, but does so rather quickly. Log outputs a new Larger timestamp than 24-bit: 0xffffff77kB