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• 10 Matomo Features You Possibly Didn’t Know About

  28 octobre 2022, par Erin

  Most users know Matomo as the privacy-focussed web analytics tool with data accuracy, superior to Google Analytics. 

  And we’re thrilled to be that — and more ! 

  At Matomo, our underlying product vision is to provide a full stack of accurate, user-friendly and privacy-mindful online marketing tools. 

  Over the years, we’ve expanded beyond baseline website statistics. Matomo Cloud users also get to benefit from additional powerful tools for audience segmentation, conversion optimisation, advanced event tracking and more. 

  Here are the top 10 advanced Matomo features you wish you knew about earlier (but won’t stop using now !). 

  Funnels

  At first glance, most customer journeys look sporadic. But every marketer will tell you that there is a method to almost every users’ madness. Or more precisely — there’s a method you can use to guide users towards conversions. 

  That’s called a customer journey — a schematic set of steps and actions people complete from developing awareness and interest in your solution to consideration and finally conversion.

  On average, 8 touchpoints are required to turn a prospect into a customer. Though the number can be significantly bigger in B2B sales and smaller for B2C Ecommerce websites. 

  With the Funnels feature, you can first map all the on-site touchpoints (desired actions) for different types of customers. Then examine the results you’re getting as prospects move through these checkbox steps.

  Funnel reports provide :

  • High-level metrics such as “Funnel conversion rate”, “Number of funnel conversions”, “Number of funnel entries”. 
  • Drilled-down reports for each funnel and each tracked action within it. This way you can track the success rates of each step and estimate their contribution to the cumulative effect.

  Segmented funnel reports for specific user cohorts (with Matomo Segmentation enabled).

  

  What makes funnels so fun (pun intended) ? The variety of use cases and configurations ! 

  You can build funnels to track conversion rates for :

  • Newsletter subscriptions
  • Job board applications 
  • Checkout or payment 
  • Product landing pages
  • Seasonal promo campaigns

  …. And pretty much any other page where users must complete a meaningful action. So go test this out. 

  Form Analytics

  On-site forms are a 101 tactic for lead generation. For most service businesses, a “contact request” or a “booking inquiry” submission means a new lead in your pipeline. 

  That said : the average on-site form conversion rates across industries stand at below 50% : 

  • Property – 37% 
  • Telecoms – 40%
  • Software — 46.83%

  That’s not bad, but it could be better. If only you could figure out why people abandon your forms….

  Oh wait, Matomo Form Analytics can supply you with answers. Form Analytics provide real-time information on key form metrics — total views, starter rate, submitter rate, conversions and more.

  

  Separately the average form hesitation time is also provided (in other words, the time a user contemplates if filling in a form is worth the effort). Plus, Matomo also tracks the time spent on form submission.

  You can review : 

  • Top drop-off fields – to understand where you are losing prospects. These fields should either be removed or simplified (e.g., with a dropdown menu) to increase conversions.
  • Most corrected-field – this will provide a clear indication of where your prospects are struggling with a form. Providing help text can simplify the process and increase conversions. 
  • Unesserary fields – with this metric, you’ll know which optional fields your leads aren’t interested in filling in and can remove them to help drive conversions. 

  With Form Analytics, you’ll be able to boost conversions and create a better on-site experience with accurate user data. 

  A/B testing

  Marketing is both an art and a science. A/B testing (or split testing) helps you statistically verify which creative ideas perform better. 

  A good conversion rate optimisation (CRO) practice is to test different elements and to do so often to find your top contenders.

  What can you split test ? Loads of things :

  • Page slogans and call-to-actions 
  • Button or submission form placements
  • Different landing page designs and layouts
  • Seasonal promo offers and banners
  • Pricing information 
  • Customer testimonial placements 

  More times than not, those small changes in page design or copy can lead to a double-digit lift in conversion rates. Accounting software Sage saw a 30% traffic boost after changing the homepage layout, copy and CTAs based on split test data. Depositphotos, in turn, got a 9.32% increase in account registration rate (CR) after testing a timed pop-up registration form. 

  The wrinkle ? A/B testing software isn’t exactly affordable, with tools averaging $119 – $1,995 per month. Plus, you then have to integrate a third-party tool with your website analytics for proper attribution — and this can get messy.

  Matomo saves you the hassle in both cases. An A/B testing tool is part of your Cloud subscription and plays nicely with other features — goal tracking, heatmaps, historic visitor profiles and more. 

  You can run split tests with Matomo on your websites or mobile apps — and find out if version A, B, C or D is the top performer. 

  

  Advertising Conversion Exports

  A well-executed search marketing or banner remarketing campaign can drive heaps of traffic to your website. But the big question is : How much of it will convert ?

  The AdTech industry has a major problem with proper attribution and, because of it, with ad fraud. 

  Globally, digital ad fraud will cost advertisers a hefty $8 billion by the end of 2022. That’s when another $74 million in ad budgets get wasted per quarter. 

  The reasons for ad budget waste may vary, but they often have a common denominator : lack of reliable conversion tracking data.

  Matomo helps you get a better sense of how you spend your cents with Advertising Conversion Reports. Unlike other MarTech analytics tools, you don’t need to embed any third-party advertising network trackers into your website or compromise user privacy.

  Instead, you can easily export accurate conversion data from Matomo (either manually via a CSV file or automated with an HTTPS link) into your Google Ads, Microsoft Advertising or Yandex Ads for cross-validation. This way you can get an objective view of the performance of different campaigns and optimise your budget allocations accordingly. 

  Find out more about tracking ad campaigns with Matomo.

  Matomo Tag Manager

  The marketing technology landscape is close to crossing 10,000 different solutions. Cross-platform advertising trackers and all sorts of customer data management tools comprise the bulk of that growing stack. 

  Remember : Each new tool embed adds extra “weight” to your web page. More tracking scripts equal slower page loading speed — and more frustration for your users. Likewise, extra embeds often means dialling up the developer (which takes time). Or tinkering with the site code yourself (which can result in errors and still raise the need to call a developer). 

  With Tag Manager, you can easily generate tags for :

  • Custom analytics reports 
  • Newsletter signups
  • Affiliates 
  • Form submission tracking 
  • Exit popups and surveys
  • Ads and more

  With Matomo Tag Manager, you can monitor, update or delete everything from one convenient interface. Finally, you can programme custom triggers — conditions when the tag gets activated — and specify data points (variables) it should collect. The latter is a great choice for staying privacy-focused and excluding any sensitive user information from processing. 

  With our tag management system (TMS), no rogue tags will mess up your analytics or conversion tracking. 

  Session recordings

  User experience (UX) plays a pivotal role in your conversion rates. 

  A five-year McKinsey study of 300 publicly listed companies found that companies with strong design practices have 32 percentage points higher revenue growth than their peers. 

  But what makes up a great website design and browsing experience ? Veteran UX designers name seven qualities :

  

  To figure out if your website meets all these criteria, you can use Session Recording — a tool for recording how users interact with your website. 

  By observing clicks, mouse moves, scrolls and form interactions you can determine problematic website design areas such as poor header navigation, subpar button placements or “boring” blocks of text. 

  Such observational studies are a huge part of the UX research process because they provide unbiased data on interaction. Or as Nielsen Norman Group puts it :

  “The way to get user data boils down to the basic rules of usability :

  • Watch what people actually do.
  • Do not believe what people say they do.
  • Definitely don’t believe what people predict they may do in the future.” 

  Most user behaviour analytics tools sell such functionality for a fee. With Matomo Cloud, this feature is included in your subscription. 

  Heatmaps

  While Session Replays provide qualitative insights, Heatmaps supply you with first-hand qualitative insights. Instead of individual user browsing sessions, you get consolidated data on where they click and how they scroll through your website. 

  

  Heatmaps are another favourite among UX designers and their CRO peers because you can :

  • Validate earlier design decisions around information architecture, page layout, button placements and so on. 
  • Develop new design hypotheses based on stats and then translate them into website design improvements. 
  • Identify distractive no-click elements that confuse users and remove them to improve conversions. 
  • Locate problematic user interface (UI) areas on specific devices or operating systems and improve them for a seamless experience.

  To get even more granular results, you can apply up to 100 Matomo segments to drill down on specific user groups, geographies or devices. 

  This way you can make data-based decisions for A/B testing, updating or redesigning your website pages. 

  Custom Alerts

  When it comes to your website, you don’t want to miss anything big — be it your biggest sales day or a sudden nosedive in traffic. 

  That’s when Custom Alerts come in handy. 

  

  With a few clicks, you can set up email or text-based alerts about important website metrics. Once you hit that metric, Matomo will send a ping. 

  You can also set different types of Custom Alerts for your teams. For example, your website administrator can get alerted about critical technical performance issues such as a sudden spike in traffic. It can indicate a DDoS attack (in the worst case) — and timely resolution is crucial here. Or suggest that your website is going viral and you might need to provision extra computing resources to ensure optimal site performance.

  Your sales team, in turn, can get alerted about new form submissions, so that they can quickly move on to lead scoring and subsequent follow-ups. 

  Use cases are plentiful with this feature. 

  Custom Dashboards and Reports

  Did you know you can get a personalised view of the main Matomo dashboards ? 

  By design, we made different website stats available as separate widgets. Hence, you can cherry-pick which stats get a prominent spot. Moreover, you can create and embed custom widgets into your Matomo dashboard to display third-party insights (e.g., POS data).

  Set up custom dashboard views for different teams, business stakeholders or clients to keep them in the loop on relevant website metrics. 

  

  Custom Reports feature, in turn, lets you slice and dice your traffic analytics the way you please. You can combine up to three different data dimensions per report and then add any number of supported metrics to get a personalised analytics report.

  For example, to zoom in on your website performance in a specific target market you can apply “location” (e.g., Germany) and “action type” (e.g., app downloads) dimensions and then get segmented data on metrics such as total visits, conversion rates, revenue and more. 

  Get to know even more ways to customise Matomo deployment.

  Roll Up Report

  Need to get aggregated traffic analytics from multiple web properties, but not ready to pay $150K per year for Google Analytics 360 for that ?

  We’ve got you with Roll-Up Reporting. You can get a 360-degree view into important KPIs like global revenue, conversion rates or form performance across multiple websites, online stores, mobile apps and even Intranet properties.

  

  Setting up this feature takes minutes, but saves you hours on manually exporting and cross-mapping data from different web analytics tools. 

  Channel all those saved hours into more productive things like increasing your conversion rates or boosting user engagement. 

  Avoid Marketing Tool Sprawl with Matomo 

  With Matomo as your website analytics and conversion optimisation app, you don’t need to switch between different systems, interfaces or have multiple tracking codes embedded on your site.

  And you don’t need to cultivate a disparate (and expensive !) MarTech tool stack — and then figure out if each of your tools is compliant with global privacy laws.

  All the tools you need are conveniently housed under one roof. 

  Want to learn more about Matomo features ? Check out product training videos next ! 

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• Using FFmpeg to stitch together H.264 videos and variably-spaced JPEG pictures ; dealing with ffmpeg warnings

  19 octobre 2022, par LB2

  Context

  


  I have a process flow that may output either H.264 Annex B streams, variably-spaced JPEGs, or a mixture of two. By variably-spaced I mean where elapsed time between any two adjacent JPEGs may (and likely to be) different from any other two adjacent JPEGs. So an example of possible inputs are :

  


  

  1. stream1.h264

  


  2. {Set of JPEGs}

  


  3. stream1.h264 + stream2.h264

  


  4. stream1.h264 + {Set of JPEGs}

  


  5. stream1.h264 + {Set of JPEGs} + stream2.h264

  


  6. stream1.h264 + {Set of JPEGs} + stream2.h264 + {Set of JPEGs} + ...

  


  7. stream1.h264 + stream2.h264 + {Set of JPEGs} + ...

  


  


  The output needs to be a single stitched (i.e. concatenated) output in MPEG-4 container.

  


  Requirements : No re-encoding or transcoding of existing video compression (One time conversion of JPEG sets to video format is OKay).

  


  Solution Prototype

  


  To prototype the solution I have found that ffmpeg has concat demuxer that would let me specify an ordered sequence of inputs that ffmpeg would then concatenate together, but all inputs must be of the same format. So, to meet that requirement, I :

  


  

  1. Convert every JPEG set to an .mp4 using concat (and using delay # directive to specify time-spacing between each JPEG)

  


  2. Convert every .h264 to .mp4 using -c copy to avoid transcoding.

  


  3. Stitch all generated interim .mp4 files into the single final .mp4 using -f concat and -c copy.

  


  


  Here's the bash script, in parts, that performs the above :

  


  


  1. Ignore the curl comment ; that's from originally generating a 100 jpeg images with numbers and these are simply saved locally. What the loop does is it generates concat input file with file sequence#.jpeg directives and duration # directive where each successive JPEG delay is incremented by 0.1 seconds (0.1 between first and second, 0.2 b/w 2nd and 3rd, 0.3 b/w 3rd and 4th, and so on). Then it runs ffmpeg command to convert the set of JPEGs to .mp4 interim file.

     


     echo "ffconcat version 1.0" >ffconcat-jpeg.txt
echo >>ffconcat-jpeg.txt

for i in {1..100}
do
    echo "file $i.jpeg" >>ffconcat-jpeg.txt
    d=$(echo "$i" | awk '{printf "%f", $1 / 10}')
    # d=$(echo "scale=2; $i/10" | bc)
    echo "duration $d" >>ffconcat-jpeg.txt
    echo "" >>ffconcat-jpeg.txt
    # curl -o "$i.jpeg" "https://math.tools/equation/get_equaimages?equation=$i&fontsize=256"
done

ffmpeg \
    -hide_banner \
    -vsync vfr \
    -f concat \
    -i ffconcat-jpeg.txt \
    -r 30 \
    -video_track_timescale 90000 \
    video-jpeg.mp4


     


  


  2. Convert two streams from .h264 to .mp4 via copy (no transcoding).

     


     ffmpeg \
    -hide_banner \
    -i low-motion-video.h264 \
    -c copy \
    -vsync vfr \
    -video_track_timescale 90000 \
    low-motion-video.mp4

ffmpeg \
    -hide_banner \
    -i full-video.h264 \
    -c copy \
    -video_track_timescale 90000 \
    -vsync vfr \
    full-video.mp4


     


  


  3. Stitch all together by generating another concat directive file.

     


     echo "ffconcat version 1.0" >ffconcat-h264.txt
echo >>ffconcat-h264.txt
echo "file low-motion-video.mp4" >>ffconcat-h264.txt
echo >>ffconcat-h264.txt
echo "file full-video.mp4" >>ffconcat-h264.txt
echo >>ffconcat-h264.txt
echo "file video-jpeg.mp4" >>ffconcat-h264.txt
echo >>ffconcat-h264.txt

ffmpeg \
    -hide_banner \
    -f concat \
    -i ffconcat-h264.txt \
    -pix_fmt yuv420p \
    -c copy \
    -video_track_timescale 90000 \
    -vsync vfr \
    video-out.mp4



     


  


  


  Problem (and attempted troubleshooting)

  


  The above does produce a reasonable output — it plays first video, then plays second video with no timing/rate issues AFAICT, then plays JPEGs with time between each JPEG "frame" growing successively, as expected.

  


  But, the conversion process produces warnings that concern me (for compatibility with players ; or potentially other IRL streams that may result in some issue my prototyping content doesn't make obvious). Initial attempts generated 100s of warnings, but with some arguments added, I reduced it down to just a handful, but this handful is stubborn and nothing I tried would help.

  


  The first conversion of JPEGs to .mp4 goes fine with the following output :

  


  Input #0, concat, from 'ffconcat-jpeg.txt':
  Duration: 00:08:25.00, start: 0.000000, bitrate: 0 kb/s
  Stream #0:0: Video: png, pal8(pc), 176x341 [SAR 3780:3780 DAR 16:31], 25 fps, 25 tbr, 25 tbn, 25 tbc
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x7fe418008e00] using SAR=1/1
[libx264 @ 0x7fe418008e00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
[libx264 @ 0x7fe418008e00] profile High 4:4:4 Predictive, level 1.3, 4:4:4, 8-bit
[libx264 @ 0x7fe418008e00] 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=4 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, mp4, to 'video-jpeg.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 176x341 [SAR 1:1 DAR 16:31], q=2-31, 30 fps, 90k tbn
    Metadata:
      encoder         : Lavc58.134.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=  100 fps=0.0 q=-1.0 Lsize=     157kB time=00:07:55.33 bitrate=   2.7kbits/s speed=2.41e+03x    
video:155kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.800846%
[libx264 @ 0x7fe418008e00] frame I:1     Avg QP:20.88  size:   574
[libx264 @ 0x7fe418008e00] frame P:43    Avg QP:14.96  size:  2005
[libx264 @ 0x7fe418008e00] frame B:56    Avg QP:21.45  size:  1266
[libx264 @ 0x7fe418008e00] consecutive B-frames: 14.0% 24.0% 30.0% 32.0%
[libx264 @ 0x7fe418008e00] mb I  I16..4: 36.4% 55.8%  7.9%
[libx264 @ 0x7fe418008e00] mb P  I16..4:  5.1%  7.5% 11.2%  P16..4:  5.6%  8.1%  4.5%  0.0%  0.0%    skip:57.9%
[libx264 @ 0x7fe418008e00] mb B  I16..4:  2.4%  0.9%  3.9%  B16..8: 16.2%  8.8%  4.6%  direct: 1.2%  skip:62.0%  L0:56.6% L1:38.7% BI: 4.7%
[libx264 @ 0x7fe418008e00] 8x8 transform intra:28.3% inter:3.7%
[libx264 @ 0x7fe418008e00] coded y,u,v intra: 26.5% 0.0% 0.0% inter: 9.8% 0.0% 0.0%
[libx264 @ 0x7fe418008e00] i16 v,h,dc,p: 82% 13%  4%  0%
[libx264 @ 0x7fe418008e00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 20%  8% 71%  1%  0%  0%  0%  0%  0%
[libx264 @ 0x7fe418008e00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 41% 11% 29%  4%  2%  3%  1%  7%  1%
[libx264 @ 0x7fe418008e00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7fe418008e00] ref P L0: 44.1%  4.2% 28.4% 23.3%
[libx264 @ 0x7fe418008e00] ref B L0: 56.2% 32.1% 11.6%
[libx264 @ 0x7fe418008e00] ref B L1: 92.4%  7.6%
[libx264 @ 0x7fe418008e00] kb/s:2.50


  


  The conversion of individual streams from .h264 to .mp4 generates two types of warnings each. One is [mp4 @ 0x7faee3040400] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly, and the other is [mp4 @ 0x7faee3040400] pts has no value.

  


  Some posts on SO (can't find my original finds on that now) suggested that it's safe to ignore and comes from H.264 being an elementary stream that supposedly doesn't contain timestamps. It surprises me a bit since I produce that stream using NVENC API and clearly supply timing information for each frame via PIC_PARAMS structure : NV_STRUCT(PIC_PARAMS, pp); ...; pp.inputTimeStamp = _frameIndex++ * (H264_CLOCK_RATE / _params.frameRate);, where #define H264_CLOCK_RATE 9000 and _params.frameRate = 30.

  


  Input #0, h264, from 'low-motion-video.h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (High), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], 30 fps, 30 tbr, 1200k tbn, 60 tbc
Output #0, mp4, to 'low-motion-video.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], q=2-31, 30 fps, 30 tbr, 90k tbn, 1200k tbc
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[mp4 @ 0x7faee3040400] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
[mp4 @ 0x7faee3040400] pts has no value
[mp4 @ 0x7faee3040400] pts has no value0kB time=-00:00:00.03 bitrate=N/A speed=N/A    
    Last message repeated 17985 times
frame=17987 fps=0.0 q=-1.0 Lsize=   79332kB time=00:09:59.50 bitrate=1084.0kbits/s speed=1.59e+03x    
video:79250kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.103804%
Input #0, h264, from 'full-video.h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (High), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], 30 fps, 30 tbr, 1200k tbn, 60 tbc
Output #0, mp4, to 'full-video.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], q=2-31, 30 fps, 30 tbr, 90k tbn, 1200k tbc
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[mp4 @ 0x7f9381864600] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
[mp4 @ 0x7f9381864600] pts has no value
[mp4 @ 0x7f9381864600] pts has no value0kB time=-00:00:00.03 bitrate=N/A speed=N/A    
    Last message repeated 17981 times
frame=17983 fps=0.0 q=-1.0 Lsize=   52976kB time=00:09:59.36 bitrate= 724.1kbits/s speed=1.33e+03x    
video:52893kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.156232%


  


  But the most worrisome error for me is from stitching together all interim .mp4 files into one :

  


  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x7f9ff2010e00] Auto-inserting h264_mp4toannexb bitstream filter
Input #0, concat, from 'ffconcat-h264.txt':
  Duration: N/A, bitrate: 1082 kb/s
  Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1440x3040 [SAR 1:1 DAR 9:19], 1082 kb/s, 30 fps, 30 tbr, 90k tbn, 60 tbc
    Metadata:
      handler_name    : VideoHandler
      vendor_id       : [0][0][0][0]
Output #0, mp4, to 'video-out.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1440x3040 [SAR 1:1 DAR 9:19], q=2-31, 1082 kb/s, 30 fps, 30 tbr, 90k tbn, 90k tbc
    Metadata:
      handler_name    : VideoHandler
      vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x7f9fe1009c00] Auto-inserting h264_mp4toannexb bitstream filter
[mp4 @ 0x7f9ff2023400] Non-monotonous DTS in output stream 0:0; previous: 53954460, current: 53954460; changing to 53954461. This may result in incorrect timestamps in the output file.
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x7f9fd1008a00] Auto-inserting h264_mp4toannexb bitstream filter
[mp4 @ 0x7f9ff2023400] Non-monotonous DTS in output stream 0:0; previous: 107900521, current: 107874150; changing to 107900522. This may result in incorrect timestamps in the output file.
[mp4 @ 0x7f9ff2023400] Non-monotonous DTS in output stream 0:0; previous: 107900522, current: 107886150; changing to 107900523. This may result in incorrect timestamps in the output file.
frame=36070 fps=0.0 q=-1.0 Lsize=  132464kB time=00:27:54.26 bitrate= 648.1kbits/s speed=6.54e+03x    
video:132296kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.126409%


  


  I'm not sure how to deal with those non-monotonous DTS errors, and no matter what I try, nothing budges. I analyzed the interim .mp4 files using ffprobe -show_frames and found that the last frame of each interim .mp4 does not have DTS, while previous frames do. E.g. :

  


  ...
[FRAME]
media_type=video
stream_index=0
key_frame=0
pkt_pts=53942461
pkt_pts_time=599.360678
pkt_dts=53942461
pkt_dts_time=599.360678
best_effort_timestamp=53942461
best_effort_timestamp_time=599.360678
pkt_duration=3600
pkt_duration_time=0.040000
pkt_pos=54161377
pkt_size=1034
width=1440
height=3040
pix_fmt=yuv420p
sample_aspect_ratio=1:1
pict_type=B
coded_picture_number=17982
display_picture_number=0
interlaced_frame=0
top_field_first=0
repeat_pict=0
color_range=unknown
color_space=unknown
color_primaries=unknown
color_transfer=unknown
chroma_location=left
[/FRAME]
[FRAME]
media_type=video
stream_index=0
key_frame=0
pkt_pts=53927461
pkt_pts_time=599.194011
pkt_dts=N/A
pkt_dts_time=N/A
best_effort_timestamp=53927461
...


  


  My guess is that as concat demuxer reads in (or elsewhere in ffmpeg's conversion pipeline), for the last frame it sees no DTS set, and produces a virtual value equal to the last seen. Then further in pipeline it consumes this input, sees that DTS value is being repeated, issues a warning and offsets it with increment by one, which might be somewhat nonsensical/unrealistic timing value.

  


  I tried using -fflags +genpts as suggested in this SO answer, but that doesn't change anything.

  


  Per yet other posts suggesting issue being with incompatible tbn and tbc values and possible timebase issues, I tried adding -time_base 1:90000 and -enc_time_base 1:90000 and -copytb 1 and nothing budges. The -video_track_timescale 90000 is there b/c it helped reduce those DTS warnings from 100s down to 3, but doesn't eliminate them all.

  


  Question

  


  What is missing and how can I get ffmpeg to perform conversions without these warnings, to be sure it produces proper, well-formed output ?

  


• Android, fast video processing

  27 avril 2016, par Ilja Kosynkin

  I have troubles in my current project which requires video processing. Basically crop function (video should be squared), trimming (video shouldn’t be longer than 30 seconds) and quality reduction (bitrate should be equal 713K).
  I’ve succesfully embedded FFmpeg into application, all functions are working quite fine except one major detail - processing as per my boss is taking too long time. For video that have around 52 MB for 36 seconds it’s taking 50 seconds to perforn all the operations (I’m trimming video to 30 seconds before any other operation obviously). The problem is that on parallel project on iOS video processing takes like 10-15 seconds for greater files. I assume that it’s related to fact that they’re using Apple QuickTime format which obviusly was developed by Apple so it’s not surprising that it’s working quite fast.
  So well, it was introduction, now my question : is there any way for Android to process any video in any quality (for now we can assume that all videos are in h264) in time of 10-15 seconds (not more then in 30 seconds, as my boss said) ? Some alternative to FFmpeg, that can perform operations faster ? I’nm pretty sure that there is no possibility to perform such work in a such short time, since I already feel like I searched thought while Internet, but I want to make sure that there is really no possibility to do such work. If anyone can provide me links to solution more faster than FFmpeg or confirm that there is no such solution, I will be very gratefull.

  Update
  Thanks to Alex Cohn I’ve resolved this with MediaCodec. After a while, I got 20 seconds processing on 52MB video with cropping to square and lowering bitrate. For any future Googlers out of here I can suggest to take a look at this respository :
  Many stuff about MediaCodec
  and more precisely at this file : Extract, edit and encode again, video and audio