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• Live stream multi-bitrate video

  2 septembre 2017, par Rishabh

  Preface

  I have read this two part tutorial (Part-1 and Part-2) by Steamroot on MPEG-DASH, and below is my understanding (please correct me if I am wrong) :

  • The video needs to be encoded into multiple bit-rates using FFmpeg.
  • The encoded videos need to be transcoded (dashified) using MP4Box.
  • The dashified videos can be served using a web server.

  Problem

  I intend to live-stream an event and I need help to understand the following :

  1. Can I club the FFmpeg and MP4Box commands into a single step ? Maybe through a wrapper program so that I do not have to run them separately ? Is there any other or better solution ?
  2. How do I send the dashified content to the web server ? FTP ? Would any vanilla web server do ?
  3. Lastly, a friend had hinted that I could also use GStreamer to achieve my objective. But, I could not find any good resource on the internet for the same. So, where (and how) does GStreamer fit in the above process ?

• FFMPEG being killed when processing H264 video

  4 mai 2017, par Stuart Clarke

  I have a program that when given a video (video.mp4), extracts the audio, a thumbnail image and the video into separate files, adds some background music to the audio file to create a new file (combined.mp3), increases the resolution of the video to 1080p and saves to a new file (videoHD.mp4), adds intro and credits to the start and end (already in 1080p) and saves that to a new file (merged.mp4) and finally combines the processed video and audio into an output file (videoExt.mp4). I’m using ffmpeg through python and subprocess.call to do all this but the raw ffmpeg commands are as follows.

  ffmpeg -y -i video.mp4 -acodec mp3 audioTrack.mp3
  
  
  
  ffmpeg -y -ss 00:00:00 -i video.mp4 -vframes 1 -q:v 2 thumb.jpg
  
  
  
  ffmpeg -y -i video.mp4 -an videoTrack.mp4
  
  
  
  ffmpeg -y -i audioTrack.mp3 -i musicTrack.mp3 -filter_complex amerge -c:a libmp3lame -q:a 4 combined.mp3
  
  
  
  ffmpeg -y -i videoTrack.mp4 -vf 'scale=1920:1080:force_original_aspect_ratio=decrease,pad=1920:1080:x=(1920-iw)/2:y=(1080-ih)/2:color=black' videoHD.mp4
  
  
  
  ffmpeg -y -f concat -i vids.ini -an merged.mp4
  
  
  
  ffmpeg -y -i merged.mp4 -i combined.mp3 -strict -2 -shortest videoExt.mp4

  Between extracting the audio and combining with the music I add a silent period matching the intro length to the beginning for the audio and lower the volume of the music for the duration of the video section and crop the music to the length of the combined intro, video and credits. This is all done with AudioSegment and works well.

  My problem is that the ffmpeg process keeps being killed. It happens in various places at various times, but always when processing the video at 1080p. I’m sure it has something to do with the H264 codec but when I use mpeg4 the quality is terrible. The videos are always less than a minute (Instagram videos) and at most 25Mb, I’m using a VPS with Ubuntu Server 15.04 installed, it should be able to handle this surely. If not is there a way around it, processing the video in parts ?

  Here is an example of the error but as I said, it can happen in various places and sometimes will complete for one command but I have never had it complete for all.

  # ffmpeg -y -f concat -i vids.ini -an merged.mp4
  
  
  
  ffmpeg version 2.5.10-0ubuntu0.15.04.1 Copyright (c) 2000-2016 the FFmpeg developers
  
    built with gcc 4.9.2 (Ubuntu 4.9.2-10ubuntu13)
  
    configuration: --prefix=/usr --extra-version=0ubuntu0.15.04.1 --build-suffix=-ffmpeg --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --shlibdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --enable-shared --disable-stripping --enable-avresample --enable-avisynth --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libopenjpeg --enable-libopus --enable-libpulse --enable-libschroedinger --enable-libshine --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libwavpack --enable-libwebp --enable-libxvid --enable-opengl --enable-x11grab --enable-libdc1394 --enable-libiec61883 --enable-libzvbi --enable-libzmq --enable-frei0r --enable-libvpx --enable-libx264 --enable-libsoxr --enable-gnutls --enable-openal --enable-libopencv --enable-librtmp --enable-libx265
  
    libavutil      54. 15.100 / 54. 15.100
  
    libavcodec     56. 13.100 / 56. 13.100
  
    libavformat    56. 15.102 / 56. 15.102
  
    libavdevice    56.  3.100 / 56.  3.100
  
    libavfilter     5.  2.103 /  5.  2.103
  
    libavresample   2.  1.  0 /  2.  1.  0
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  1.100 /  1.  1.100
  
    libpostproc    53.  3.100 / 53.  3.100
  
  Input #0, concat, from 'vids.ini':
  
    Duration: N/A, start: 0.000000, bitrate: 2999 kb/s
  
      Stream #0:0: Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1920x1080 [SAR 1:1 DAR 16:9], 2996 kb/s, 29.97 fps, 29.97 tbr, 11988 tbn, 59.94 tbc
  
      Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 2 kb/s
  
  [libx264 @ 0x134fd00] using SAR=1/1
  
  [libx264 @ 0x134fd00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
  
  [libx264 @ 0x134fd00] profile High, level 4.0
  
  [libx264 @ 0x134fd00] 264 - core 142 r2495 6a301b6 - H.264/MPEG-4 AVC codec - Copyleft 2003-2014 - 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=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 'merged.mp4':
  
    Metadata:
  
      encoder         : Lavf56.15.102
  
      Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 1920x1080 [SAR 1:1 DAR 16:9], q=-1--1, 29.97 fps, 11988 tbn, 29.97 tbc
  
      Metadata:
  
        encoder         : Lavc56.13.100 libx264
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  
  Press [q] to stop, [?] for help
  
  frame=   13 fps=0.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A dup=1 dro
  
  frame=   28 fps= 27 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A dup=1 dro
  
  frame=   46 fps= 21 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A dup=1 dro
  
  frame=   51 fps= 16 q=29.0 size=     120kB time=00:00:00.-3 bitrate=N/A dup=1 dr
  
  frame=   55 fps= 15 q=29.0 size=     154kB time=00:00:00.10 bitrate=12574.5kbits
  
  frame=   60 fps= 14 q=29.0 size=     195kB time=00:00:00.26 bitrate=5981.0kbits/
  
  frame=   65 fps= 13 q=29.0 size=     254kB time=00:00:00.43 bitrate=4797.0kbits/
  
  frame=   69 fps= 12 q=29.0 size=     287kB time=00:00:00.56 bitrate=4142.1kbits/
  
  frame=   73 fps= 12 q=29.0 size=     375kB time=00:00:00.70 bitrate=4384.2kbits/
  
  frame=   76 fps= 11 q=29.0 size=     418kB time=00:00:00.80 bitrate=4278.4kbits/
  
  frame=   81 fps= 11 q=29.0 size=     441kB time=00:00:00.96 bitrate=3732.4kbits/
  
  frame=   85 fps= 10 q=29.0 size=     472kB time=00:00:01.10 bitrate=3511.0kbits/
  
  Killed1 drop=0 

  Any ideas or if anyone knows a better way to this please let me know.

  Cheers,

  Stu

• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.