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• How to Implement Cross-Channel Analytics : A Guide for Marketers

  17 avril 2024, par Erin

  Every modern marketer knows they have to connect with consumers across several channels. But do you know how well Instagram works alongside organic traffic or your email list ? Are you even tracking the impacts of these channels in one place ?

  You need a cross-channel analytics solution if you answered no to either of these questions. 

  In this article, we’ll explain cross-channel analytics, why your company probably needs it and how to set up a cross-channel analytics solution as quickly and easily as possible.

  What is cross-channel analytics ? 

  Cross-channel analytics is a form of marketing analytics that collects and analyses data from every channel and campaign you use.

  The result is a comprehensive view of your customer’s journey and each channel’s role in converting customers. 

  Cross-channel analytics lets you track every channel you use to convert customers, including :

  • Your website
  • Social media profiles
  • Email
  • Paid search
  • E-commerce
  • Retargeting campaigns

  Cross-channel analytics solves one of the most significant issues of cross-channel or multi-channel marketing efforts : measurement. 

  Research shows that only 16% of marketing tech stacks allow for accurate measurement of multi-channel initiatives across channels. 

  That’s a problem, given the staggering number of touchpoints in a typical buyer’s conversion path. However, it can be fixed using a cross-channel analytics approach that lets you measure the performance of every channel and assign a dollar value to its role in every conversion. 

  The difference between cross-channel analytics and multi-channel analytics

  Cross-channel analytics and multi-channel analytics sound very similar, but there’s one key difference you need to know. Multi-channel analytics measures the performance of several channels, but not necessarily all of them, nor the extent to which they work together to drive conversions. Conversely, cross-channel analytics measures the performance of all your marketing channels and how they work together. 

  What are the benefits of cross-channel analytics 

  Cross-channel analytics offers a lot of marketing and business benefits. Here are the ones marketing managers love most.

  Get a complete view of the customer journey

  Implementing a cross-channel analytics solution is the only way to get a complete view of your customer journey. 

  Cross-channel marketing analytics lets you see your customer journey in high definition, allowing you to build comprehensive customer profiles using data from multiple sources across every touchpoint. 

  

  The result ? You get to understand how every customer behaves at every point of the customer journey, why they convert or leave your funnel, and which channels play the biggest role. 

  In short, you get to see why customers convert so you can learn how to convert more of them.

  Personalise the customer experience

  According to a McKinsey study, customers demand personalisation, and brands that excel at it generate 40% more revenue. Deliver the personalisation they desire and reap the benefits with cross-channel analytics. 

  When you understand the customer journey in detail, it becomes much easier to personalise your website and marketing efforts to their preferences and behaviours.

  Identify your most effective marketing channels

  Cross-channel marketing helps you understand your marketing efforts to see how every channel impacts conversions. 

  Take a look at the screenshot from Matomo below. Cross-channel analytics lets you get incredibly granular — we can see the number of conversions of organic search drives and the performance of individual search engines. 

  

  This makes it easy to identify your most effective marketing channels and allocate your resources appropriately. It also allows you to ask (and answer) which channels are the most effective.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Attribute conversions accurately 

  An attribution model decides how you assign credit for each customer conversion to different touchpoints on the customer journey. Without a cross-channel analytics solution, you’re stuck using a standard attribution model like first or last click. 

  These models will show you how customers first found your brand or which channel finally convinced them to convert, but it doesn’t help you understand the role all your channels played in the conversion. 

  Cross-channel analytics solves this attribution problem. Rather than attributing a conversion to the touchpoint that directly led to the sale, cross-channel data gives you the real picture and allows you to use multi-touch attribution to understand which touchpoints generate the most revenue.

  How to set up cross-channel analytics

  Now that you know what cross-channel analytics is and why you should use it, here’s how to set up your solution. 

  1. Determine your objectives

  Defining your marketing goals will help you build a more relevant and actionable cross-channel analytics solution. 

  If you want to improve marketing attribution, for example, you can choose a platform with that feature built-in. If you care about personalisation, you could choose a platform with A/B testing capabilities to measure the impact of your personalisation efforts. 

  1. Set relevant KPIs

  You’ll want to track relevant KPIs to measure the marketing effectiveness of each channel. Put top-of-the-funnel metrics aside and focus on conversion metrics. 

  These include :

  • Conversion rate
  • Average visit duration
  • Bounce rate

  1. Implement tracking and analytics tools

  Gathering customer data from every channel and centralising it in a single location is one of the biggest challenges of cross-channel analytics. Still, it’s made easier with the right tracking tool or analytics platform. 

  The trick is to choose a platform that lets you measure as many of your channels as possible in a single platform. With Matomo, for example, you can track search, paid search, social and email campaigns and your website analytics.

  1. Set up a multi-touch attribution model

  Now that you have all of your data in one place, you can set up a multi-touch attribution model that lets you understand the extent to which each marketing channel contributes to your overall success. 

  There are several attribution models to choose from, including :

  

  Each model has benefits and drawbacks, so choosing the right model for your organisation can be tricky. Rather than take a wild guess, evaluate each model against your marketing objectives, sales length cycle and data availability.

  For example, if you want to focus on optimising customer acquisition costs, a model that prioritises earlier touchpoints will be better. If you care about conversions, you might try a time decay model. 

  1. Turn data into insights with reports

  One of the big benefits of choosing a tool like Matomo, which consolidates data in one place, is that it significantly speeds up and simplifies reporting.

  When all the data is stored in one platform, you don’t need to spend hours combing through your social media platforms and copying and pasting analytics data into a spreadsheet. It’s all there and ready for you to run reports.

  Try Matomo for Free

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

  Start for free

  No credit card required

  1. Take action

  There’s no point implementing a cross-channel analytics system if you aren’t going to take action. 

  But where should you start ?

  Optimising your budgets and prioritising marketing spend is a great starting point. Use your cross-channel insights to find your most effective marketing channels (they’re the ones that convert the most customers or have the highest ROI) and allocate more of your budget to them. 

  You can also optimise the channels that aren’t pulling their weight if social media is letting you down ; for example, experiment with tactics like social commerce that could drive more conversions. Alternatively, you could choose to stop investing entirely in these channels.

  Cross-channel analytics best practices

  If you already have a cross-channel analytics solution, take things to the next level with the following best practices. 

  Use a centralised solution to track everything

  Centralising your data in one analytics tool can streamline your marketing efforts and help you stay on top of your data. It won’t just save you from tabbing between different browsers or copying and pasting everything into a spreadsheet, but it can also make it easier to create reports. 

  Think about consumer privacy 

  If you are looking at a new cross-channel analytics tool, consider how it accounts for data privacy regulations in your area. 

  You’re going to be collecting a lot of data, so it’s important to respect their privacy wishes. 

  It’s best to choose a platform like Matomo that complies with the strictest privacy laws (CCPA, GDPR, etc.).

  Monitor data in real time

  So, you’ve got a holistic view of your marketing efforts by integrating all your channels into a single tool ?

  Great, now go further by monitoring the impact of your marketing efforts in real time.

  

  With a web analytics platform like Matomo, you can see who visits your site, what they do, and where they come from through features like the visits log report, which even lets you view individual user sessions. This lets you measure the impact of posting on a particular social channel or launching a new offer. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  Reallocate marketing budgets based on performance

  When you track every channel, you can use a multi-touch attribution model like position-based or time-decay to give every channel the credit it deserves. But don’t just credit each channel ; turn your valuable insights into action. 

  Use cross-channel attribution analytics data to reallocate your marketing budget to the most profitable channels or spend time optimising the channels that aren’t pulling their weight. 

  Cross-channel analytics platforms to get started with 

  The marketing analytics market is huge. Mordor Intelligence valued it at $6.31 billion in 2024 and expects it to reach $11.54 billion by 2029. Many of these platforms offer cross-channel analytics, but few can track the impact of multiple marketing channels in one place. 

  So, rather than force you to trawl through confusing product pages, we’ve shortlisted three of the best cross-channel analytics solutions. 

  Matomo

  

  Matomo is a web analytics platform that lets you collect and centralise your marketing data while giving you 100% accurate data. That includes search, social, e-commerce, campaign tracking data and comprehensive website analytics.

  Better still, you get the necessary tools to turn those insights into action. Custom reporting lets you track and visualise the metrics that matter, while conversion optimisation tools like built-in A/B testing, heatmaps, session recordings and more let you test your theories. 

  Google Analytics

  

  Google Analytics is the most popular and widely used tool on the market. The level of analysis and customisation you can do with it is impressive for a free tool. That includes tracking just about any event and creating reports from scratch. 

  Google Analytics provides some cross-channel marketing features and lets you track the impact of various channels, such as social and search, but there are a couple of drawbacks. 

  Privacy can be a concern because Google Analytics collects data from your customers for its own remarketing purposes. 

  It also uses data sampling to generate wider insights from a small subset of your data. This lack of accurate data reporting can cause you to generate false insights.

  With Google Analytics, you’ll also need to subscribe to additional tools to gain advanced insights into the user experience. So, consider that while this tool is free, you’ll need to pay for heatmaps, session recording and A/B testing tools to optimise effectively.

  Improvado

  

  Improvado is an analytics tool for sales and marketing teams that extracts thousands of metrics from hundreds of sources. It centralises data in data warehouses, from which you can create a range of marketing dashboards.

  While Improvado does have analytics capabilities, it is primarily an ETL (extraction, transform, load) tool for organisations that want to centralise all their data. That means marketers who aren’t familiar with data transformations may struggle to get their heads around the complexity of the platform.

  Make the most of cross-channel analytics with Matomo

  Cross-channel analytics is the only way to get a comprehensive view of your customer journey and understand how your channels work together to drive conversions.

  Then you’re dealing with so many channels and data ; keeping things as simple as possible is the key to success. That’s why over 1 million websites choose Matomo. 

  Our all-in-one analytics solution measures traditional web analytics, behavioural analytics, attribution and SEO, so you have 100% accurate data in one place. 

  Try it free for 21 days. No credit card required.

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• Blank video as the result of converting from bmp ffmpeg [duplicate]

  1er juin 2021, par Артур Клочко

  ---

  


  Update

  


  I'm sorry, as I wrote I tried to play video via different variants and got nothing, but now I send it to myself via Telegram app, and it displays there correctly. It plays via Chrome also. Seems it is not the ffmpeg problem.

  


  I uploaded it to my site, if you are not aware, please check it via stupid uwp app or Windows Media player if you are using Windows, and if you have the same problem, please feedback and I will report it as a bug

  


  https://okumaima.com/cave.mp4

  


  ---

  


  Recently I have been using ffmpeg to convert jpg file set to mp4, and it was successfully. I used the next .bat file to do it :

  


  ffmpeg -framerate 60 -i out\%%d.jpg render.mp4


  


  Now, I am trying to do the same, but with bmp files, generated as screenshots using WinApi. Images as themselves are absolutely correct - I can open them via photos app, or anything else. Images resolution is constant and equals 2002x773.

  


  So now I am using the same .bat to make video :

  


  ffmpeg -framerate 60 -i out\%%d.bmp render.mp4


  


  It generates a tiny-size video (17 kb, for 10 bmp images, 5 mb each), that is opening by UWP video app or Windows Media program, but nothing is happening - no errors, yet no video length. If there are more images, the result video size also increases, but the video still doesn't play.

  


  Full output from ffmpeg :

  


  ffmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
  built with gcc 9.2.1 (GCC) 20200122
  configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
Input #0, image2, from 'rnd\%d.bmp':
  Duration: 00:00:00.17, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: bmp, bgra, 2002x773, 60 tbr, 60 tbn, 60 tbc
Stream mapping:
  Stream #0:0 -> #0:0 (bmp (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 052fed40] using cpu capabilities: MMX2 SSE2Fast LZCNT SSSE3 SSE4.2
[libx264 @ 052fed40] profile High 4:4:4 Predictive, level 4.2, 4:4:4, 8-bit
[libx264 @ 052fed40] 264 - core 159 - H.264/MPEG-4 AVC codec - Copyleft 2003-2019 - 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=4 threads=6 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, mp4, to 'caev.mp4':
  Metadata:
    encoder         : Lavf58.29.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv444p, 2002x773, q=-1--1, 60 fps, 15360 tbn, 60 tbc
    Metadata:
      encoder         : Lavc58.54.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
frame=   10 fps=0.0 q=-1.0 Lsize=      16kB time=00:00:00.11 bitrate=1142.4kbits/s speed=0.151x
video:15kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 6.097639%
[libx264 @ 052fed40] frame I:1     Avg QP:18.80  size:  6840
[libx264 @ 052fed40] frame P:4     Avg QP:25.73  size:  1517
[libx264 @ 052fed40] frame B:5     Avg QP:35.54  size:   426
[libx264 @ 052fed40] consecutive B-frames: 20.0% 40.0%  0.0% 40.0%
[libx264 @ 052fed40] mb I  I16..4: 21.1% 76.3%  2.6%
[libx264 @ 052fed40] mb P  I16..4:  0.6%  0.3%  0.3%  P16..4:  0.9%  0.6%  0.1%  0.0%  0.0%    skip:97.0%
[libx264 @ 052fed40] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  4.0%  0.2%  0.0%  direct: 0.0%  skip:95.6%  L0:34.5% L1:64.6% BI: 0.9%
[libx264 @ 052fed40] 8x8 transform intra:73.8% inter:10.2%
[libx264 @ 052fed40] coded y,u,v intra: 1.8% 1.5% 1.5% inter: 0.1% 0.1% 0.1%
[libx264 @ 052fed40] i16 v,h,dc,p: 52% 47%  1%  0%
[libx264 @ 052fed40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 47% 45%  8%  0%  0%  0%  0%  0%  0%
[libx264 @ 052fed40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 31% 32%  3%  0%  1%  2%  5%  2%
[libx264 @ 052fed40] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 052fed40] ref P L0: 75.8%  4.1% 15.8%  4.3%
[libx264 @ 052fed40] ref B L0: 63.5% 34.8%  1.7%
[libx264 @ 052fed40] ref B L1: 97.6%  2.4%
[libx264 @ 052fed40] kb/s:721.78


  


• Create 1 video using image with length of audio

  19 septembre 2017, par Vishnu

  I am trying to create video with the length of an audio file , and 1 single image as the background for whole video.

  I use following code

  'ffmpeg -y -loop 1 -f image2 -r 24 -i subtitle.jpg  -i audio.mp3 -c:v libx264 -c:a copy -shortest video.flv';

  But problem is processing time is too slow , and sometime video length is not same as audio length.Can some one suggest me alternative ideas.

  p.s : audio length is just 3 to 8 seconds.

  Below is my output

  ffmpeg version N-83443-gc03029a Copyright (c) 2000-2017 the FFmpeg
  developers built with gcc 4.8.5 (GCC) 20150623 (Red Hat 4.8.5-11)
  configuration : —enable-gpl —enable-version3 —enable-libfdk_aac
  —enable-libmp3lame —enable-libtheora —enable-libvorbis —enable-libvpx —enable-libx264 —enable-nonfree —enable-libopencore-amrnb —enable-libopencore-amrwb —enable-libgsm —enable-libxvid —disable-static —enable-shared libavutil 55. 46.100 / 55. 46.100 libavcodec 57. 75.100 / 57. 75.100 libavformat 57. 66.101
  / 57. 66.101 libavdevice 57. 2.100 / 57. 2.100 libavfilter 6. 73.100 /
  6. 73.100 libswscale 4. 3.101 / 4. 3.101 libswresample 2. 4.100 / 2. 4.100 libpostproc 54. 2.100 / 54. 2.100 Input #0, image2, from ’subtitle/1.jpg’ : Duration : 00:00:00.04, start : 0.000000, bitrate : N/A
  Stream #0:0 : Video : mjpeg, yuvj420p(pc, bt470bg/unknown/unknown),
  1280x720 [SAR 1:1 DAR 16:9], 24 fps, 24 tbr, 24 tbn, 24 tbc [mp3 @
  0x1013ee0] Estimating duration from bitrate, this may be inaccurate
  Input #1, mp3, from ’audio/1.mp3’ : Metadata : encoder : Lavf57.71.100
  Duration : 00:00:05.98, start : 0.000000, bitrate : 32 kb/s Stream #1:0 :
  Audio : mp3, 22050 Hz, mono, s16p, 32 kb/s No pixel format specified,
  yuvj420p for H.264 encoding chosen. Use -pix_fmt yuv420p for
  compatibility with outdated media players. [libx264 @ 0x1020600] using
  SAR=1/1 [libx264 @ 0x1020600] using cpu capabilities : MMX2 SSE2Fast
  SSSE3 SSE4.2 AVX [libx264 @ 0x1020600] profile High, level 3.1
  [libx264 @ 0x1020600] 264 - core 148 r2762 90a61ec - H.264/MPEG-4 AVC
  codec - Copyleft 2003-2017 - 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=3 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=24 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 ’video/1.flv’ : Metadata : encoder :
  Lavf57.66.101 Stream #0:0 : Video : h264 (libx264) ([7][0][0][0] /
  0x0007), yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=-1—1, 24 fps,
  1k tbn, 24 tbc Metadata : encoder : Lavc57.75.100 libx264 Side data :
  cpb : bitrate max/min/avg : 0/0/0 buffer size : 0 vbv_delay : -1 Stream

  0:1 : Audio : mp3 ([2][0][0][0] / 0x0002), 22050 Hz, mono, s16p, 32 kb/s Stream mapping : Stream #0:0 -> #0:0 (mjpeg (native) -> h264

  (libx264)) Stream #1:0 -> #0:1 (copy) Press [q] to stop, [?] for help
  [image2 @ 0x1011680] Thread message queue blocking ; consider raising
  the thread_queue_size option (current value : 8) frame= 32 fps=0.0
  q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x frame= 53 fps=
  51 q=28.0 size= 210kB time=00:00:00.13 bitrate=13157.8kbits/s
  speed=0.126x frame= 73 fps= 47 q=28.0 size= 216kB time=00:00:00.96
  bitrate=1828.9kbits/s speed=0.622x frame= 93 fps= 45 q=28.0 size=
  221kB time=00:00:01.79 bitrate=1009.9kbits/s speed=0.859x frame= 109
  fps= 42 q=28.0 size= 225kB time=00:00:02.45 bitrate= 750.6kbits/s
  speed=0.95x frame= 126 fps= 40 q=28.0 size= 230kB time=00:00:03.16
  bitrate= 594.3kbits/s speed=1.02x frame= 145 fps= 40 q=28.0 size=
  235kB time=00:00:03.95 bitrate= 485.7kbits/s speed=1.09x frame= 162
  fps= 39 q=28.0 size= 239kB time=00:00:04.67 bitrate= 419.3kbits/s
  speed=1.12x frame= 181 fps= 39 q=28.0 size= 244kB time=00:00:05.46
  bitrate= 366.5kbits/s speed=1.17x frame= 193 fps= 33 q=28.0 Lsize=
  248kB time=00:00:05.95 bitrate= 340.4kbits/s speed=1.03x video:217kB
  audio:23kB subtitle:0kB other streams:0kB global headers:0kB muxing
  overhead : 2.834417% [libx264 @ 0x1020600] frame I:1 Avg QP:17.19
  size:212155 [libx264 @ 0x1020600] frame P:48 Avg QP:18.02 size : 140
  [libx264 @ 0x1020600] frame B:144 Avg QP:27.33 size : 43 [libx264 @
  0x1020600] consecutive B-frames : 0.5% 0.0% 0.0% 99.5% [libx264 @
  0x1020600] mb I I16..4 : 3.1% 86.8% 10.2% [libx264 @ 0x1020600] mb P
  I16..4 : 0.0% 0.0% 0.0% P16..4 : 1.0% 0.0% 0.0% 0.0% 0.0% skip:99.0%
  [libx264 @ 0x1020600] mb B I16..4 : 0.0% 0.0% 0.0% B16..8 : 0.1% 0.0%
  0.0% direct : 0.0% skip:99.8% L0 : 9.1% L1:90.9% BI : 0.0% [libx264 @ 0x1020600] 8x8 transform intra:82.0% inter:55.7% [libx264 @ 0x1020600]
  coded y,uvDC,uvAC intra : 87.6% 80.7% 62.5% inter : 0.0% 0.2% 0.0%
  [libx264 @ 0x1020600] i16 v,h,dc,p : 76% 22% 2% 1% [libx264 @
  0x1020600] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu : 11% 15% 24% 7% 9% 7% 10% 6%
  10% [libx264 @ 0x1020600] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu : 23% 27% 9% 7%
  8% 6% 8% 5% 8% [libx264 @ 0x1020600] i8c dc,h,v,p : 55% 23% 14% 8%
  [libx264 @ 0x1020600] Weighted P-Frames : Y:0.0% UV:0.0% [libx264 @
  0x1020600] ref P L0 : 95.1% 0.1% 3.2% 1.6% [libx264 @ 0x1020600] ref B
  L0 : 91.7% 3.3% 5.0% [libx264 @ 0x1020600] ref B L1 : 86.1% 13.9%
  [libx264 @ 0x1020600] kb/s:223.84 0