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• D3D11 hardware screen recording with ffmpeg using Intel H264 QSV hardware encoding

  13 mai 2024, par Cactus

  I'm trying to screen record using ffmpeg and having it all done via hardware on my Intel integrated graphics.

  


  Specs

  


  For reference, here are my specs :

  


  

  • Windows 11 Pro Version 10.0.22631 Build 22631

  


  • Model : Dell Inc. OptiPlex 3090

  


  • Processor : 11th Gen Intel(R) Core(TM) i3-1115G4 @ 3.00GHz, 2995 Mhz, 2 Core(s), 4 Logical Processor(s)

  


  • Terminal : Windows Terminal using Windows Powershell

  


  


  In this wiki, it looked like I could use the "Windows 8+ Desktop Duplication API" to be "directly encoded by a compatible hardware encoder" — so I thought that I could use h264_qsv to encode, allowing me to record without much performance overhead costs.

  


  Attempt 1

  


  I've tried copying one of the command examples, but it didn't work :

  


  ffmpeg -init_hw_device d3d11va -filter_complex ddagrab=0 


  


  Output :

  


  ffmpeg version 2024-05-02-git-71669f2ad5-full_build-www.gyan.dev Copyright (c) 2000-2024 the FFmpeg developers
  built with gcc 13.2.0 (Rev5, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libxevd --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxeve --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-dxva2 --enable-d3d11va --enable-d3d12va --enable-ffnvcodec --enable-libvpl --enable-nvdec --enable-nvenc --enable-vaapi --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      59. 16.101 / 59. 16.101
  libavcodec     61.  5.103 / 61.  5.103
  libavformat    61.  3.103 / 61.  3.103
  libavdevice    61.  2.100 / 61.  2.100
  libavfilter    10.  2.101 / 10.  2.101
  libswscale      8.  2.100 /  8.  2.100
  libswresample   5.  2.100 /  5.  2.100
  libpostproc    58.  2.100 / 58.  2.100
Stream mapping:
  ddagrab:default -> Stream #0:0 (h264_qsv)
Press [q] to stop, [?] for help
Impossible to convert between the formats supported by the filter 'Parsed_ddagrab_0' and the filter 'auto_scale_0'
[fc#0 @ 000001e4ed4393c0] Error configuring filter graph: Function not implemented
[fc#0 @ 000001e4ed4393c0] Task finished with error code: -40 (Function not implemented)
[fc#0 @ 000001e4ed4393c0] Terminating thread with return code -40 (Function not implemented)
[vost#0:0/h264_qsv @ 000001e4ed454000] Could not open encoder before EOF
[vost#0:0/h264_qsv @ 000001e4ed454000] Task finished with error code: -22 (Invalid argument)
[vost#0:0/h264_qsv @ 000001e4ed454000] Terminating thread with return code -22 (Invalid argument)
[out#0/matroska @ 000001e4ed453280] Nothing was written into output file, because at least one of its streams received no packets.
frame=    0 fps=0.0 q=0.0 Lsize=       0KiB time=N/A bitrate=N/A speed=N/A
Conversion failed!


  


  Attempt 2

  


  I then tried copying one of the command examples in the linked ddagrab wiki page, but also no luck.

  


  ffmpeg -f lavfi -i ddagrab -c:v h264_qsv output.mkv


  


  Output :

  


  ffmpeg version 2024-05-02-git-71669f2ad5-full_build-www.gyan.dev Copyright (c) 2000-2024 the FFmpeg developers
  built with gcc 13.2.0 (Rev5, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libxevd --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxeve --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-dxva2 --enable-d3d11va --enable-d3d12va --enable-ffnvcodec --enable-libvpl --enable-nvdec --enable-nvenc --enable-vaapi --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      59. 16.101 / 59. 16.101
  libavcodec     61.  5.103 / 61.  5.103
  libavformat    61.  3.103 / 61.  3.103
  libavdevice    61.  2.100 / 61.  2.100
  libavfilter    10.  2.101 / 10.  2.101
  libswscale      8.  2.100 /  8.  2.100
  libswresample   5.  2.100 /  5.  2.100
  libpostproc    58.  2.100 / 58.  2.100
Input #0, lavfi, from 'ddagrab':
  Duration: N/A, start: 0.000000, bitrate: N/A
  Stream #0:0: Video: wrapped_avframe, d3d11, 1920x1080 [SAR 1:1 DAR 16:9], 30 fps, 30 tbr, 1000k tbn
File 'output.mkv' already exists. Overwrite? [y/N] y
Stream mapping:
  Stream #0:0 -> #0:0 (wrapped_avframe (native) -> h264 (h264_qsv))
Press [q] to stop, [?] for help
Impossible to convert between the formats supported by the filter 'Parsed_null_0' and the filter 'auto_scale_0'
[vf#0:0 @ 00000115e105bd40] Error reinitializing filters!
[vf#0:0 @ 00000115e105bd40] Task finished with error code: -40 (Function not implemented)
[vf#0:0 @ 00000115e105bd40] Terminating thread with return code -40 (Function not implemented)
[vost#0:0/h264_qsv @ 00000115e105aa00] Could not open encoder before EOF
[vost#0:0/h264_qsv @ 00000115e105aa00] Task finished with error code: -22 (Invalid argument)
[vost#0:0/h264_qsv @ 00000115e105aa00] Terminating thread with return code -22 (Invalid argument)
[out#0/matroska @ 00000115e1054b80] Nothing was written into output file, because at least one of its streams received no packets.
frame=    0 fps=0.0 q=0.0 Lsize=       0KiB time=N/A bitrate=N/A speed=N/A
Conversion failed!


  


  My main problem is that I don't know how to use really any of these encoders and the examples I've found above don't work with me. What command should I use to screen record all with hardware acceleration on my Intel system ? I don't care about any presets or other options at the moment.

  


  Is there also a way to record audio ? When I tried to use -f dshow -i audio="Stereo Mix (Realtek(R) Audio)" by itself, which I made sure was turned on in Windows' settings and named correctly, it didn't work (is it because I'm using Bluetooth headphones that isn't connected to Realtek's system ?). Should this question be made separately than this one ?

  


• avfilter/dnn : Refactor DNN parameter configuration system

  7 mai 2024, par Zhao Zhili

  avfilter/dnn : Refactor DNN parameter configuration system
  This patch trying to resolve mulitiple issues related to parameter
  
  configuration :
  Firstly, each DNN filters duplicate DNN_COMMON_OPTIONS, which should
  
  be the common options of backend.
  Secondly, backend options are hidden behind the scene. It's a
  
  AV_OPT_TYPE_STRING backend_configs for user, and parsed by each
  
  backend. We don't know each backend support what kind of options
  
  from the help message.
  Third, DNN backends duplicate DNN_BACKEND_COMMON_OPTIONS.
  Last but not the least, pass backend options via AV_OPT_TYPE_STRING
  
  makes it hard to pass AV_OPT_TYPE_BINARY to backend, if not impossible.
  This patch puts backend common options and each backend options inside
  
  DnnContext to reduce code duplication, make options user friendly, and
  
  easy to extend for future usecase.
  For example,
  ./ffmpeg -h filter=dnn_processing
  dnn_processing AVOptions :
  
     dnn_backend       <int>        ..FV....... DNN backend (from INT_MIN to INT_MAX) (default tensorflow)
  
       tensorflow      1            ..FV....... tensorflow backend flag
  
       openvino        2            ..FV....... openvino backend flag
  
       torch           3            ..FV....... torch backend flag
  dnn_base AVOptions :
  
     model             <string>     ..F........ path to model file
  
     input             <string>     ..F........ input name of the model
  
     output            <string>     ..F........ output name of the model
  
     backend_configs   <string>     ..F.......P backend configs (deprecated)
  
     options           <string>     ..F.......P backend configs (deprecated)
  
     nireq             <int>        ..F........ number of request (from 0 to INT_MAX) (default 0)
  
     async             <boolean>    ..F........ use DNN async inference (default true)
  
     device            <string>     ..F........ device to run model
  dnn_tensorflow AVOptions :
  
     sess_config       <string>     ..F........ config for SessionOptions
  dnn_openvino AVOptions :
  
     batch_size        <int>        ..F........ batch size per request (from 1 to 1000) (default 1)
  
     input_resizable   <boolean>    ..F........ can input be resizable or not (default false)
  
     layout            <int>        ..F........ input layout of model (from 0 to 2) (default none)
  
       none            0            ..F........ none
  
       nchw            1            ..F........ nchw
  
       nhwc            2            ..F........ nhwc
  
     scale             <float>      ..F........ Add scale preprocess operation. Divide each element of input by specified value. (from INT_MIN to INT_MAX) (default 0)
  
     mean              <float>      ..F........ Add mean preprocess operation. Subtract specified value from each element of input. (from INT_MIN to INT_MAX) (default 0)
  dnn_th AVOptions :
  
     optimize          <int>        ..F........ turn on graph executor optimization (from 0 to 1) (default 0)
  Signed-off-by : Zhao Zhili <zhilizhao@tencent.com>
  
  Reviewed-by : Wenbin Chen <wenbin.chen@intel.com>
  
  Reviewed-by : Guo Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_common.h
  • [DH] libavfilter/dnn/dnn_backend_openvino.c
  • [DH] libavfilter/dnn/dnn_backend_tf.c
  • [DH] libavfilter/dnn/dnn_backend_torch.cpp
  • [DH] libavfilter/dnn/dnn_interface.c
  • [DH] libavfilter/dnn_filter_common.c
  • [DH] libavfilter/dnn_filter_common.h
  • [DH] libavfilter/dnn_interface.h
  • [DH] libavfilter/vf_derain.c
  • [DH] libavfilter/vf_dnn_classify.c
  • [DH] libavfilter/vf_dnn_detect.c
  • [DH] libavfilter/vf_dnn_processing.c
  • [DH] libavfilter/vf_sr.c

• B2B Marketing Attribution Guide : How to Master It in 2024

  21 mai 2024, par Erin

  The last thing you want is to invest your advertising dollars in channels, campaigns and ads that don’t work. But B2B marketing attribution — figuring out which marketing efforts drive revenue — is far from easy.

  With longer sales funnels and multiple people from the same company involved in the same sales process, B2B (business-to-business) is a different ballgame from B2C (business-to-consumer) marketing.

  In this guide, we break down what B2B marketing attribution is, how it’s different, which tools you can use to set it up and the best practices.

  What is B2B marketing attribution ?

  Marketing attribution in B2B companies is about figuring out where your high-value leads come from — nailing down long customer journeys across many different touchpoints.

  

  The goal is to determine which campaigns and content contributed to various parts of the customer journey. It’s a complex process that needs a reliable, privacy-focused web analytics tool and a CRM that integrates with it.

  This process significantly differs from traditional marketing attribution, where you focus more on short sales cycles from individual customers. With multiple contributing decision makers, B2B attribution requires more robust systems.

  What makes marketing attribution different for B2B ?

  The key differences between B2B and B2C marketing attribution are a longer sales funnel and more people involved in the sales process.

  The B2B sales funnel is significantly longer and more complex

  The typical B2C sales funnel is often broken down into four simple stages :

  1. Awareness : when a prospect first finds out about your product or brand
  2. Interest : where a prospect starts to learn about the benefits of your product
  3. Desire : when a prospect understands that they need your product
  4. Action : the actual process of closing the sale

  Even the most simplified B2B sales funnel includes several key stages.

  

  Here’s a brief overview of each :

  1. Awareness : Buyers recognise they have a problem and start looking for solutions. Stand out with blog posts, social media updates, ebooks and whitepapers.
  2. Consideration : Buyers are aware of your company and are comparing options. Provide product demos, webinars and case studies to address their concerns and build trust.
  3. Conversion : Buyers have chosen your product or company. Offer live demos, customer service, case studies and testimonials to finalise the purchase.
  4. Loyalty : Buyers have made a purchase and are now customers. Nurture relationships with thank you emails, follow-ups, how-tos, reward programs and surveys to encourage repeat business.
  5. Advocacy : Loyal customers become advocates, promoting your brand to others. Encourage this with surveys, testimonial requests and a referral program.

  A longer sales cycle typically involves not only more touchpoints but also extended decision-making processes.

  More teams are involved in the marketing and sales process

  The last differentiation in B2B attribution is the number of people involved. Instead of clear-cut sales and marketing teams, revenue teams are becoming more common.

  They include all go-to-market teams like sales, marketing, customer success and customer support. In B2B sales, long-term customer relationships can be incredibly valuable. As such, the focus shifts away from new customer acquisition alone.

  For example, you can also track and optimise your onboarding process. Marketing gets involved in post-sale efforts to boost loyalty. Sales reps follow up with customer success to get new sales angles and insights. Customer support insights drive future product development.

  Everyone works together to meet high-level company goals.

  The next section will explore how to set up an attribution system.

  How to find the right mix of B2B marketing attribution tools

  For most B2B marketing teams, the main struggle with attribution is not with the strategy but with creating a reliable system that gives them the data points they need to implement that strategy.

  We’ll outline one approach you can take to achieve this without a million-dollar budget or internal data science team.

  Use website analytics to track touchpoints

  The first thing you want to do is install a reliable website analytics solution on your website. 

  Once you’ve got your analytics in place, use campaign tracking parameters to track touchpoints from external campaigns like email newsletters, social media ads, review sites (like Capterra) and third-party partner campaigns.

  This way, you get a clear picture of which sources are driving traffic and conversions, helping you improve your marketing strategies.

  With analytics installed, you can track the referring sources of visits, engagement and conversion events. A robust solution like Matomo tracks everything from traffic sources, marketing attribution and visitor counts to behavioural analytics, like clicks, scrolling patterns and form interactions on your site.

  Marketing attribution will give you a cohesive view of which traffic sources and campaigns drive conversions and revenue over long periods. With Matomo’s marketing attribution feature, you can even use different marketing attribution models to compare results :

  

  For example, in a single report, you can compare the last interaction, first interaction and linear (three common marketing attribution models). 

  In total, Matomo has 6 available attribution models to choose from :

  1. First interaction
  2. Last interaction
  3. Last non-direct 
  4. Linear
  5. Position based
  6. Time decay 

  These additional attribution models are crucial for B2B sites. While other web analytics solutions often limit to last-click attribution, this model isn’t optimal for B2B with extended sales cycles.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

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  Use a CRM to integrate customer data from multiple sources

  Use your CRM software to integrate customer data from multiple sources. This will give you the ability to get meaningful B2B marketing insights. For example, you can get company-level insights so you can view conversion information by company, not just by person.

  Done effectively, you can close the loop back to analytics data by integrating data from multiple teams and platforms. 

  Implement self-reported attribution

  To further enhance the data, add qualifying questions in the lead signup process to create a hybrid attribution model. This is also known as self-reported attribution.

  

  Your web analytics platform won’t always be able to track the source of certain visits — for instance, “dark social” or peer-to-peer sharing, where links are shared privately and are not easily traceable by analytics tools.

  Doing self-reported attribution is crucial for getting a holistic image of your customer journey. 

  However, self-reported attribution isn’t foolproof ; users may click randomly or inaccurately recall where they first heard about you. So it’s essential to blend this data with your analytics to gain a more accurate understanding.

  Best practices for handling B2B prospect data in a privacy-sensitive world 

  Lastly, it’s important to respect your prospects’ privacy and comply with privacy regulations when conducting B2B marketing attribution.

  Privacy regulations and their enforcement are rapidly gaining momentum around the globe. Meta recently received a record GDPR fine of €1.2 billion for insufficient privacy measures when handling user data by the Irish Data Protection Agency.

  If you don’t want to risk major fines (or customers feeling betrayed), you shouldn’t follow in the same footsteps.

  Switch to a privacy-friendly web analytics

  Instead of using a controversial solution like Google Analytics, use a privacy-friendly web analytics solution like Matomo, Fathom or Plausible. 

  These alternatives not only ensure compliance with regulations like GDPR but also provide peace of mind amid the uncertain relationship between Google and GDPR. Google Analytics has faced bans in recent years, raising concerns about the future of the solution.

  While organisations governed by GDPR can currently use Google Analytics, there’s no guarantee of its continued availability.

  Make the switch to privacy-friendly web analytics to avoid potential fines and disruptive rulings that could force you to change platforms urgently. Such disruptions can be catastrophic for marketing teams heavily reliant on web analytics for tracking campaigns, business goals and marketing efforts.

  Improve your B2B marketing attribution with Matomo

  Matomo’s privacy-by-design architecture makes it the perfect analytics platform for the modern B2B marketer. Matomo enables you to meet even the strictest privacy regulations.

  At the same time, through campaign tracking URLs, marketing attribution, integrations and our API, you can track the results of various marketing channels and campaigns effectively. We help you understand the impact of each dollar of your marketing budget. 

  If you want a competitive edge over other B2B companies, try Matomo for free for 21 days. No credit card required.

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