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• Google Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.

• Unveiling GA4 Issues : 8 Questions from a Marketer That GA4 Can’t Answer

  8 janvier 2024, par Alex

  It’s hard to believe, but Universal Analytics had a lifespan of 11 years, from its announcement in March 2012. Despite occasional criticism, this service established standards for the entire web analytics industry. Many metrics and reports became benchmarks for a whole generation of marketers. It truly was an era.

  For instance, a lot of marketers got used to starting each workday by inspecting dashboards and standard traffic reports in the Universal Analytics web interface. There were so, so many of those days. They became so accustomed to Universal Analytics that they would enter reports, manipulate numbers, and play with metrics almost on autopilot, without much thought.

  However, six months have passed since the sunset of Universal Analytics – precisely on July 1, 2023, when Google stopped processing requests for resources using the previous version of Google Analytics. The time when data about visitors and their interactions with the website were more clearly structured within the UA paradigm is now in the past. GA4 has brought a plethora of opportunities to marketers, but along with those opportunities came a series of complexities.

  GA4 issues

  Since its initial announcement in 2020, GA4 has been plagued with errors and inconsistencies. It still has poor and sometimes illogical documentation, numerous restrictions, and peculiar interface solutions. But more importantly, the barrier to entry into web analytics has significantly increased.

  If you diligently follow GA4 updates, read the documentation, and possess skills in working with data (SQL and basic statistics), you probably won’t feel any problems – you know how to set up a convenient and efficient environment for your product and marketing data. But what if you’re not that proficient ? That’s when issues arise.

  In this article, we try to address a series of straightforward questions that less experienced users – marketers, project managers, SEO specialists, and others – want answers to. They have no time to delve into the intricacies of GA4 but seek access to the fundamentals crucial for their functionality.

  Previously, in Universal Analytics, they could quickly and conveniently address their issues. Now, the situation has become, to put it mildly, more complex. We’ve identified 8 such questions for which the current version of GA4 either fails to provide answers or implies that answers would require significant enhancements. So, let’s dive into them one by one.

  Question 1 : What are the most popular traffic sources on my website ?

  Seemingly a straightforward question. What does GA4 tell us ? It responds with a question : “Which traffic source parameter are you interested in ?”

  

  Wait, what ?

  People just want to know which resources bring them the most traffic. Is that really an issue ?

  Unfortunately, yes. In GA4, there are not one, not two, but three traffic source parameters :

  1. Session source.
  2. First User Source – the source of the first session for each user.
  3. Just the source – determined at the event or conversion level.

  If you wanted to open a report and draw conclusions quickly, we have bad news for you. Before you start ranking your traffic sources by popularity, you need to do some mental work on which parameter and in what context you will look. And even when you decide, you’ll need to make a choice in the selection of standard reports : work with the User Acquisition Report or Traffic Acquisition.

  Yes, there is a difference between them : the first uses the First User Source parameter, and the second uses the session source. And you need to figure that out too.

  Question 2 : What is my conversion rate ?

  This question concerns everyone, and it should be simple, implying a straightforward answer. But no.

  

  In GA4, there are three conversion metrics (yes, three) :

  1. Session conversion – the percentage of sessions with a conversion.
  2. User conversion – the percentage of users who completed a conversion.
  3. First-time Purchaser Conversion – the share of active users who made their first purchase.

  If the last metric doesn’t interest us much, GA4 users can still choose something from the remaining two. But what’s next ? Which parameters to use for comparison ? Session source or user source ? What if you want to see the conversion rate for a specific event ? And how do you do this in analyses rather than in standard reports ?

  In the end, instead of an answer to a simple question, marketers get a bunch of new questions.

  Question 3. Can I trust user and session metrics ?

  Unfortunately, no. This may boggle the mind of those not well-versed in the mechanics of calculating user and session metrics, but it’s the plain truth : the numbers in GA4 and those in reality may and will differ.

  

  The reason is that GA4 uses the HyperLogLog++ statistical algorithm to count unique values. Without delving into details, it’s a mechanism for approximate estimation of a metric with a certain level of error.

  This error level is quite well-documented. For instance, for the Total Users metric, the error level is 1.63% (for a 95% confidence interval). In simple terms, this means that 100,000 users in the GA4 interface equate to 100,000 1.63% in reality.

  Furthermore – but this is no surprise to anyone – GA4 samples data. This means that with too large a sample size or when using a large number of parameters, the application will assess your metrics based on a partial sample – let’s say 5, 10, or 30% of the entire population.

  It’s a reasonable assumption, but it can (and probably will) surprise marketers – the metrics will deviate from reality. All end-users can do (excluding delving into raw data methodologies) is to take this error level into account in their conclusions.

  Question 4. How do I calculate First Click attribution ?

  You can’t. Unfortunately, as of late, GA4 offers only three attribution models available in the Attribution tab : Last Click, Last Click For Google Ads, and Data Driven. First Click attribution is essential for understanding where and when demand is generated. In the previous version of Google Analytics (and until recently, in the current one), users could quickly apply First Click and other attribution models, compare them, and gain insights. Now, this capability is gone.

  

  Certainly, you can look at the conversion distribution considering the First User Source parameter – this will be some proxy for First Click attribution. However, comparing it with others in the Model Comparison tab won’t be possible. In the context of the GA4 interface, it makes sense to forget about non-standard attribution models.

  Question 5. How do I account for intra-session traffic ?

  Intra-session traffic essentially refers to a change in traffic sources within a session. Imagine a scenario where a user comes to your site organically from Google and, within a minute, comes from an email campaign. In the previous version of Google Analytics, a new session with the traffic source “e-mail” would be created in such a case. But now, the situation has changed.

  A session now only ends in the case of a timeout – say, 30 minutes without interaction. This means a session will always have a source from which it started. If a user changes the source within a session (clicks on an ad, from email campaigns, and so on), you won’t know anything about it until they convert. This is a significant blow to intra-session traffic since their contribution to traffic remains virtually unnoticed. 

  Question 6. How can I account for users who have not consented to the use of third-party cookies ?
  

  You can’t. Google Consent Mode settings imply several options when a user rejects the use of 3rd party cookies. In GA4 and BigQuery, depersonalized cookieless pings will be sent. These pings do not contain specific client_id, session_id, or other custom dimensions. As a result, you won’t be able to consider them as users or link the actions of such users together.

  Question 7. How can I compare data in explorations with the previous year ?

  The maximum data retention period for a free GA4 account is 14 months. This means that if the date range is wider, you can only use standard reports. You won’t be able to compare or view cohorts or funnels for periods more than 14 months ago. This makes the product functionality less rich because various report formats in explorations are very convenient for comparing specific metrics in easily digestible reports.

  

  Of course, you always have the option to connect BigQuery and store raw data without limitations, but this process usually requires the involvement of an advanced analyst. And precisely this option is unavailable to most marketers in small teams.

  Question 8. Is the data for yesterday accurate ?

  Unknown. Google declares that data processing in GA4 takes up to 48 hours. And although this process is faster, most users still have room for frustration. And they can be understood.

  

  What does “data processing takes 24-48 hours” mean ? When will the data in reports be complete ? For yesterday ? Or the day before yesterday ? Or for all days that were more than two days ago ? Unclear. What should marketers tell their managers when they were asked if all the data is in this report ? Well, probably all of it… or maybe not… Let’s wait for 48 hours…

  Undoubtedly, computational resources and time are needed for data preprocessing and aggregation. It’s okay that data for today will not be up-to-date. And probably not for yesterday either. But people just want to know when they can trust their data. Are they asking for too much : just a note that this report contains all the data sent and processed by Google Analytics ?

  What should you do ?

  Credit should be given to the Google team – they have done a lot to enable users to answer these questions in one form or another. For example, you can use data streaming in BigQuery and work with raw data. The entry threshold for this functionality has been significantly lowered. In fact, if you are dissatisfied with the GA4 interface, you can organize your export to BigQuery and create your own reports without (almost) any restrictions.

  Another strong option is the widespread launch of GTM Server Side. This allows you to quite freely modify the event model and essentially enrich each hit with various parameters, doing this in a first-party context. This, of course, reduces the harmful impact of most of the limitations described in this text.

  But this is not a solution.

  The users in question – marketers, managers, developers – they do not want or do not have the time for a deep dive into the issue. And they want simple answers to simple (it seemed) questions. And for now, unfortunately, GA4 is more of a professional tool for analysts than a convenient instrument for generating insights for not very advanced users.

  Why is this such a serious issue ?

  The thing is – and this is crucial – over the past 10 years, Google has managed to create a sort of GA-bubble for marketers. Many of them have become so accustomed to Google Analytics that when faced with another issue, they don’t venture to explore alternative solutions but attempt to solve it on their own. And almost always, this turns out to be expensive and inconvenient.

  However, with the latest updates to GA4, it is becoming increasingly evident that this application is struggling to address even the most basic questions from users. And these questions are not fantastically complex. Much of what was described in this article is not an unsolvable mystery and is successfully addressed by other analytics services.

  Let’s try to answer some of the questions described from the perspective of Matomo.

  Question 1 : What are the most popular traffic sources ? [Solved]

  In the Acquisition panel, you will find at least three easily identifiable reports – for traffic channels (All Channels), sources (Websites), and campaigns (Campaigns). 

  

  With these, you can quickly and easily answer the question about the most popular traffic sources, and if needed, delve into more detailed information, such as landing pages.

  Question 2 : What is my conversion rate ? [Solved]

  Under Goals in Matomo, you’ll easily find the overall conversion rate for your site. Below that you’ll have access to the conversion rate of each goal you’ve set in your Matomo instance.

  

  Question 3 : Can I trust user and session metrics ? [Solved]

  Yes. With Matomo, you’re guaranteed 100% accurate data. Matomo does not apply sampling, does not employ specific statistical algorithms, or any analogs of threshold values. Yes, it is possible, and it’s perfectly normal. If you see a metric in the visits or users field, it accurately represents reality by 100%.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Question 4 : How do I calculate First Click attribution ? [Solved]
  
  You can do this in the same section where the other 5 attribution models, available in Matomo, are calculated – in the Multi Attribution section.

  

  You can choose a specific conversion and, in a few clicks, calculate and compare up to 3 marketing attribution models. This means you don’t have to spend several days digging through documentation trying to understand how a particular model is calculated. Have a question – get an answer.

  Question 5 : How do I account for intra-session traffic ? [Solved]

  Matomo creates a new visit when a user changes a campaign. This means that you will accurately capture all relevant traffic if it is adequately tagged. No campaigns will be lost within a visit, as they will have a new utm_campaign parameter.

  This is a crucial point because when the Referrer changes, a new visit is not created, but the key lies in something else – accounting for all available traffic becomes your responsibility and depends on how you tag it.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Question 6 : How can I account for users who have not consented to the use of third-party cookies ? [Solved]

  Google Analytics requires users to accept a cookie consent banner with “analytics_storage=granted” to track them. If users reject cookie consent banners, however, then Google Analytics can’t track these visitors at all. They simply won’t show up in your traffic reports. 

  Matomo doesn’t require cookie consent banners (apart from in the United Kingdom and Germany) and can therefore continue to track visitors even after they have rejected a cookie consent screen. This is achieved through a config_id variable (the user identifier equivalent which is updating once a day). 

  

  This means that virtually all of your website traffic will be tracked regardless of whether users accept a cookie consent banner or not.

  Question 7 : How can I compare data in explorations with the previous year ? [Solved]
  

  There is no limitation on data retention for your aggregated reports in Matomo. The essence of Matomo experience lies in the reporting data, and consequently, retaining reports indefinitely is a viable option. So you can compare data for any timeframe. 7

  

• ffmpeg massive error spamming from FritzBox rtsp stream

  19 février 2019, par itzaiiro

  Im trying to offer a rtsp live TV stream via rtmp to my family, since the Fritz Box (which is offering the stream) has only 4 tuners -> at most 4 streams can be watched simultaneously.
  Im using ffmpeg to prepare the stream as dash stream and send it to my rtmp nginx. When i run ffmpeg im experiencing heavy image and audio artifacts in the final stream and error spams of doom in the console. I couldn’t find anything specific to my case on google. I read on the internet that AVM barely implemented the rtsp protocoll enough to get it to work with vlc mediaplayer.

  launch param :

  ffmpeg -i "rtsp://192.168.178.1:554/?avm=1&freq=114&bw=8&msys=dvbc&mtype=256qam&sr=6900&specinv=1&pids=0,16,17,18,20,260,543,544,546,548,1621" -sn -vcodec libx264 -vprofile baseline -acodec aac -strict -2 -b:v 500k -minrate 500k -maxrate 500k -bufsize 1000k -g 60 -s 640x360 -f flv rtmp://192.168.178.15/dash/pro_sieben_low -sn -vcodec libx264 -vprofile baseline -acodec aac -strict -2 -b:v 1500k -minrate 1500k -maxrate 1500k -bufsize 3000k -g 60 -s 1280x720 -f flv rtmp://192.168.178.15/dash/pro_sieben_med -sn -vcodec libx264 -vprofile baseline -acodec aac -strict -2 -b:v 5000k -minrate 5000k -maxrate 5000k -bufsize 10000k -g 60 -s 1920x1080 -f flv rtmp://192.168.178.15/dash/pro_sieben_high

  ffmpeg output (windows) :
  stored in pastebin
  https://pastebin.com/p4HAyBi5

  Is there anyway to get this under control ? The original stream is running good with vlc, but its unwatchable after its out of ffmpeg.

  Edit :
  I was running/testing this on my windows machine, but my target for this task is a ubuntu 16.04 so here ffmpeg on target with pthread support :

  ffmpeg -i "rtsp://192.168.178.1:554/?avm=1&freq=114&bw=8&msys=dvbc&mtype=256qam&sr=6900&specinv=1&pids=0,16,17,18,20,260,543,544,546,548,1621" -sn -vcodec libx264 -vprofile baseline -acodec aac -strict -2 -b:v 500k -minrate 500k -maxrate 500k -bufsize 1000k -g 60 -s 640x360 -f flv rtmp://192.168.178.15/dash/pro_sieben_low -sn -vcodec libx264 -vprofile baseline -acodec aac -strict -2 -b:v 1500k -minrate 1500k -maxrate 1500k -bufsize 3000k -g 60 -s 1280x720 -f flv rtmp://192.168.178.15/dash/pro_sieben_med -sn -vcodec libx264 -vprofile baseline -acodec aac -strict -2 -b:v 5000k -minrate 5000k -maxrate 5000k -bufsize 10000k -g 60 -s 1920x1080 -f flv rtmp://192.168.178.15/dash/pro_sieben_high
  
  ffmpeg version 2.8.15-0ubuntu0.16.04.1 Copyright (c) 2000-2018 the FFmpeg developers
  
    built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.10) 20160609
  
    configuration: --prefix=/usr --extra-version=0ubuntu0.16.04.1 --build-suffix=-ffmpeg --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --cc=cc --cxx=g++ --enable-gpl --enable-shared --disable-stripping --disable-decoder=libopenjpeg --disable-decoder=libschroedinger --enable-avresample --enable-avisynth --enable-gnutls --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-librtmp --enable-libschroedinger --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxvid --enable-libzvbi --enable-openal --enable-opengl --enable-x11grab --enable-libdc1394 --enable-libiec61883 --enable-libzmq --enable-frei0r --enable-libx264 --enable-libopencv
  
    libavutil      54. 31.100 / 54. 31.100
  
    libavcodec     56. 60.100 / 56. 60.100
  
    libavformat    56. 40.101 / 56. 40.101
  
    libavdevice    56.  4.100 / 56.  4.100
  
    libavfilter     5. 40.101 /  5. 40.101
  
    libavresample   2.  1.  0 /  2.  1.  0
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  2.101 /  1.  2.101
  
    libpostproc    53.  3.100 / 53.  3.100
  
  [mpeg2video @ 0x167cde0] Invalid frame dimensions 0x0.
  
      Last message repeated 10 times
  
  [rtsp @ 0x1627c20] Could not find codec parameters for stream 4 (Unknown: none ([5][0][0][0] / 0x0005)): unknown codec
  
  Consider increasing the value for the 'analyzeduration' and 'probesize' options
  
  Input #0, rtsp, from 'rtsp://192.168.178.1:554/?avm=1&freq=114&bw=8&msys=dvbc&mtype=256qam&sr=6900&specinv=1&pids=0,16,17,18,20,260,543,544,546,548,1621':
  
    Metadata:
  
      title           : SatIPServer:1 0,0,4
  
    Duration: N/A, start: 33786.528778, bitrate: N/A
  
    Program 12101
  
      Metadata:
  
        service_name    : ?▒RTL▒ Television
  
        service_provider: ?Unitymedia
  
    Program 12102
  
      Metadata:
  
        service_name    : ?SAT.1
  
        service_provider: ?Unitymedia
  
    Program 12103
  
      Metadata:
  
        service_name    : ?ProSieben
  
        service_provider: ?Unitymedia
  
      Stream #0:3: Video: mpeg2video (Main) ([2][0][0][0] / 0x0002), yuv420p(tv), 720x576 [SAR 64:45 DAR 16:9], max. 15000 kb/s, 25 fps, 25 tbr, 90k tbn, 50 tbc
  
      Stream #0:2(deu): Audio: mp2 ([3][0][0][0] / 0x0003), 48000 Hz, stereo, s16p, 192 kb/s (clean effects)
  
      Stream #0:0(deu): Audio: ac3 ([6][0][0][0] / 0x0006), 48000 Hz, 5.1(side), fltp, 384 kb/s (clean effects)
  
      Stream #0:1(deu,deu): Subtitle: dvb_teletext ([6][0][0][0] / 0x0006), 492x250
  
      Stream #0:4: Unknown: none ([5][0][0][0] / 0x0005)
  
    Program 12104
  
      Metadata:
  
        service_name    : ?VOX
  
        service_provider: ?Unitymedia
  
    Program 12105
  
      Metadata:
  
        service_name    : ?RTL2
  
        service_provider: ?Unitymedia
  
    Program 12106
  
      Metadata:
  
        service_name    : ?kabel eins
  
        service_provider: ?Unitymedia
  
    Program 12107
  
      Metadata:
  
        service_name    : ?▒S▒uper▒ RTL▒
  
        service_provider: ?Unitymedia
  
    Program 12109
  
      Metadata:
  
        service_name    : ?ntv
  
        service_provider: ?Unitymedia
  
    Program 12113
  
      Metadata:
  
        service_name    : ?ProSieben MAXX
  
        service_provider: ?Unitymedia
  
    Program 20116
  
      Metadata:
  
        service_name    : ?SIXX
  
        service_provider: ?Unitymedia
  
  [libx264 @ 0x182b140] using SAR=1/1
  
  [libx264 @ 0x182b140] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 AVX2 LZCNT BMI2
  
  [libx264 @ 0x182b140] profile Constrained Baseline, level 3.0
  
  [libx264 @ 0x182b140] 264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=0 ref=3 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=0 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=0 weightp=0 keyint=60 keyint_min=6 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=cbr mbtree=1 bitrate=500 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 vbv_maxrate=500 vbv_bufsize=1000 nal_hrd=none filler=0 ip_ratio=1.40 aq=1:1.00
  
  [libx264 @ 0x16e03c0] using SAR=1/1
  
  [libx264 @ 0x16e03c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 AVX2 LZCNT BMI2
  
  [libx264 @ 0x16e03c0] profile Constrained Baseline, level 3.1
  
  [libx264 @ 0x16e03c0] 264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=0 ref=3 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=0 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=0 weightp=0 keyint=60 keyint_min=6 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=cbr mbtree=1 bitrate=1500 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 vbv_maxrate=1500 vbv_bufsize=3000 nal_hrd=none filler=0 ip_ratio=1.40 aq=1:1.00
  
  [libx264 @ 0x16cc880] using SAR=1/1
  
  [libx264 @ 0x16cc880] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 AVX2 LZCNT BMI2
  
  [libx264 @ 0x16cc880] profile Constrained Baseline, level 4.0
  
  [libx264 @ 0x16cc880] 264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=0 ref=3 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=0 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=0 weightp=0 keyint=60 keyint_min=6 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=cbr mbtree=1 bitrate=5000 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 vbv_maxrate=5000 vbv_bufsize=10000 nal_hrd=none filler=0 ip_ratio=1.40 aq=1:1.00
  
  Output #0, flv, to 'rtmp://192.168.178.15/dash/pro_sieben_low':
  
    Metadata:
  
      title           : SatIPServer:1 0,0,4
  
      encoder         : Lavf56.40.101
  
      Stream #0:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p, 640x360 [SAR 1:1 DAR 16:9], q=-1--1, 500 kb/s, 25 fps, 1k tbn, 25 tbc
  
      Metadata:
  
        encoder         : Lavc56.60.100 libx264
  
      Stream #0:1(deu): Audio: aac ([10][0][0][0] / 0x000A), 48000 Hz, 5.1(side), fltp, 128 kb/s (clean effects)
  
      Metadata:
  
        encoder         : Lavc56.60.100 aac
  
  Output #1, flv, to 'rtmp://192.168.178.15/dash/pro_sieben_med':
  
    Metadata:
  
      title           : SatIPServer:1 0,0,4
  
      encoder         : Lavf56.40.101
  
      Stream #1:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p, 1280x720 [SAR 1:1 DAR 16:9], q=-1--1, 1500 kb/s, 25 fps, 1k tbn, 25 tbc
  
      Metadata:
  
        encoder         : Lavc56.60.100 libx264
  
      Stream #1:1(deu): Audio: aac ([10][0][0][0] / 0x000A), 48000 Hz, 5.1(side), fltp, 128 kb/s (clean effects)
  
      Metadata:
  
        encoder         : Lavc56.60.100 aac
  
  Output #2, flv, to 'rtmp://192.168.178.15/dash/pro_sieben_high':
  
    Metadata:
  
      title           : SatIPServer:1 0,0,4
  
      encoder         : Lavf56.40.101
  
      Stream #2:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p, 1920x1080 [SAR 1:1 DAR 16:9], q=-1--1, 5000 kb/s, 25 fps, 1k tbn, 25 tbc
  
      Metadata:
  
        encoder         : Lavc56.60.100 libx264
  
      Stream #2:1(deu): Audio: aac ([10][0][0][0] / 0x000A), 48000 Hz, 5.1(side), fltp, 128 kb/s (clean effects)
  
      Metadata:
  
        encoder         : Lavc56.60.100 aac
  
  Stream mapping:
  
    Stream #0:3 -> #0:0 (mpeg2video (native) -> h264 (libx264))
  
    Stream #0:0 -> #0:1 (ac3 (native) -> aac (native))
  
    Stream #0:3 -> #1:0 (mpeg2video (native) -> h264 (libx264))
  
    Stream #0:0 -> #1:1 (ac3 (native) -> aac (native))
  
    Stream #0:3 -> #2:0 (mpeg2video (native) -> h264 (libx264))
  
    Stream #0:0 -> #2:1 (ac3 (native) -> aac (native))
  
  Press [q] to stop, [?] for help
  
  RTP: missed 2137 packets.0 q=26.0 q=23.0 size=     238kB time=00:00:04.91 bitrate= 397.3kbits/s
  
  [rtsp @ 0x1627c20] PES packet size mismatch
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 16
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 17
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 18
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 19
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 20
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 21
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 22
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 23
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 17 11
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 24
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 25
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 26
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 27
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 31
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 32
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 33
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 34
  
  [mpeg2video @ 0x16d72c0] invalid mb type in I Frame at 0 35
  
  [mpeg2video @ 0x16d72c0] Warning MVs not available
  
  [mpeg2video @ 0x16d72c0] concealing 1125 DC, 1125 AC, 1125 MV errors in I frame
  
  RTP: missed 11 packets
  
  RTP: missed 37 packets
  
  [ac3 @ 0x1676bc0] exponent out-of-range
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  [ac3 @ 0x1676bc0] exponent out-of-range
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  RTP: missed 21 packets
  
  RTP: missed 32 packets
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 1 4
  
  [mpeg2video @ 0x16d72c0] Warning MVs not available
  
  [mpeg2video @ 0x16d72c0] concealing 1080 DC, 1080 AC, 1080 MV errors in B frame
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 21 27
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 2 26
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 0 27
  
  [mpeg2video @ 0x16d72c0] invalid cbp -1 at 2 1
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 5 2
  
  [mpeg2video @ 0x16d72c0] skip with previntra
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 2 4
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 1 6
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 3 7
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 37 9
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 1 10
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 1 31
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 5 32
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 1 33
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 26 35
  
  [mpeg2video @ 0x16d72c0] Warning MVs not available
  
  [mpeg2video @ 0x16d72c0] concealing 945 DC, 945 AC, 945 MV errors in B frame
  
  [rtsp @ 0x1627c20] PES packet size mismatchze=     294kB time=00:00:05.27 bitrate= 457.1kbits/s
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  [ac3 @ 0x1676bc0] exponent out-of-range
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  RTP: missed 38 packets25.0 q=26.0 q=22.0 size=     320kB time=00:00:11.18 bitrate= 234.3kbits/s
  
  RTP: missed 18 packets
  
  RTP: missed 9 packets
  
  RTP: missed 21 packets
  
  RTP: missed 9 packets
  
  [rtsp @ 0x1627c20] PES packet size mismatch
  
  [ac3 @ 0x1676bc0] exponent out-of-range
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  [ac3 @ 0x1676bc0] bandwidth code = 63 > 60
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  RTP: missed 13 packets
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 3 6
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 1 16
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 0 17
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 5 21
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 3 27
  
  [mpeg2video @ 0x16d72c0] invalid cbp -1 at 7 32
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 8 32
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
      Last message repeated 2 times
  
  [mpeg2video @ 0x16d72c0] Warning MVs not available
  
  [mpeg2video @ 0x16d72c0] concealing 810 DC, 810 AC, 810 MV errors in P frame
  
  RTP: missed 44 packets
  
  [rtsp @ 0x1627c20] PES packet size mismatch
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  [ac3 @ 0x1676bc0] exponent out-of-range0 size=     338kB time=00:00:11.43 bitrate= 242.2kbits/s
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  RTP: missed 35 packets
  
      Last message repeated 1 times
  
  RTP: missed 31 packets
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  [ac3 @ 0x1676bc0] exponent out-of-range
  
  [ac3 @ 0x1676bc0] error decoding the audio block
  
  [ac3 @ 0x1676bc0] frame sync error
  
  Error while decoding stream #0:0: Invalid data found when processing input
  
  RTP: missed 48 packets
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 5 23
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 17 24
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
      Last message repeated 1 times
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 5 27
  
  [mpeg2video @ 0x16d72c0] skip with previntra
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 15 29
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 8 31
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 13 32
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 22 33
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 20 34
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 17 35
  
  [mpeg2video @ 0x16d72c0] Warning MVs not available
  
  [mpeg2video @ 0x16d72c0] concealing 543 DC, 543 AC, 543 MV errors in B frame
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 16 1
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 11 13
  
  [mpeg2video @ 0x16d72c0] invalid mb type in P Frame at 4 7
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 2 8
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 0 19
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 0 20
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 9 21
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 2 22
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 10 23
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
  [mpeg2video @ 0x16d72c0] invalid mb type in P Frame at 1 25
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 38 26
  
  [mpeg2video @ 0x16d72c0] invalid mb type in P Frame at 3 27
  
  [mpeg2video @ 0x16d72c0] invalid mb type in P Frame at 22 28
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 16 29
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 5 30
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 14 31
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 2 32
  
  [mpeg2video @ 0x16d72c0] invalid mb type in P Frame at 19 9
  
  [mpeg2video @ 0x16d72c0] invalid mb type in P Frame at 11 10
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 13 11
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 8 12
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 33 13
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 8 14
  
  [mpeg2video @ 0x16d72c0] invalid cbp -1 at 6 15
  
  [mpeg2video @ 0x16d72c0] invalid cbp -1 at 15 19
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 9 18
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] invalid cbp -1 at 15 21
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 13 21
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 16 22
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 5 23
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 4 24
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 7 26
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 9 13
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 44 14
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 2 15
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
      Last message repeated 1 times
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 34 18
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 15 19
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 8 21
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 14 22
  
  [mpeg2video @ 0x16d72c0] invalid cbp -1 at 9 23
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 4 24
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
      Last message repeated 1 times
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
  [mpeg2video @ 0x16d72c0] ac-tex damaged at 25 29
  
  [mpeg2video @ 0x16d72c0] invalid cbp 0 at 14 30
  
  [mpeg2video @ 0x16d72c0] mb incr damaged
  
      Last message repeated 1 times
  
  [mpeg2video @ 0x16d72c0] 00 motion_type at 35 33
  
  [mpeg2video @ 0x16d72c0] slice mismatch
  
  [mpeg2video @ 0x16d72c0] Warning MVs not available
  
  [mpeg2video @ 0x16d72c0] concealing 1350 DC, 1350 AC, 1350 MV errors in P frame
  
  [flv @ 0x16d7c40] Failed to update header with correct duration.:00:12.12 bitrate= 241.4kbits/s
  
  [flv @ 0x16d7c40] Failed to update header with correct filesize.
  
  [flv @ 0x16df5a0] Failed to update header with correct duration.
  
  [flv @ 0x16df5a0] Failed to update header with correct filesize.
  
  [flv @ 0x16cbe00] Failed to update header with correct duration.
  
  [flv @ 0x16cbe00] Failed to update header with correct filesize.
  
  frame=  136 fps= 12 q=-1.0 Lq=-1.0 q=-1.0 size=     633kB time=00:00:13.24 bitrate= 391.8kbits/s
  
  video:7049kB audio:272kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
  
  [libx264 @ 0x182b140] frame I:4     Avg QP:19.84  size: 33269
  
  [libx264 @ 0x182b140] frame P:132   Avg QP:21.93  size:  3136
  
  [libx264 @ 0x182b140] mb I  I16..4:  6.9%  0.0% 93.1%
  
  [libx264 @ 0x182b140] mb P  I16..4:  0.1%  0.0%  0.7%  P16..4: 32.0% 11.6%  4.4%  0.0%  0.0%    skip:51.1%
  
  [libx264 @ 0x182b140] coded y,uvDC,uvAC intra: 91.1% 88.6% 67.5% inter: 14.8% 20.2% 1.5%
  
  [libx264 @ 0x182b140] i16 v,h,dc,p: 35% 15%  3% 47%
  
  [libx264 @ 0x182b140] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 31% 19%  9%  6%  7%  8%  7%  7%  7%
  
  [libx264 @ 0x182b140] i8c dc,h,v,p: 45% 20% 28%  7%
  
  [libx264 @ 0x182b140] ref P L0: 80.5% 11.2%  8.4%
  
  [libx264 @ 0x182b140] kb/s:369.61
  
  [libx264 @ 0x16e03c0] frame I:4     Avg QP:19.81  size: 77367
  
  [libx264 @ 0x16e03c0] frame P:132   Avg QP:21.64  size:  9825
  
  [libx264 @ 0x16e03c0] mb I  I16..4: 16.2%  0.0% 83.8%
  
  [libx264 @ 0x16e03c0] mb P  I16..4:  0.6%  0.0%  1.2%  P16..4: 34.1%  9.5%  2.9%  0.0%  0.0%    skip:51.8%
  
  [libx264 @ 0x16e03c0] coded y,uvDC,uvAC intra: 76.8% 77.3% 41.8% inter: 11.9% 20.4% 0.7%
  
  [libx264 @ 0x16e03c0] i16 v,h,dc,p: 38% 18%  7% 37%
  
  [libx264 @ 0x16e03c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 30% 22% 10%  5%  7%  7%  7%  6%  6%
  
  [libx264 @ 0x16e03c0] i8c dc,h,v,p: 47% 21% 26%  6%
  
  [libx264 @ 0x16e03c0] ref P L0: 80.8% 12.1%  7.1%
  
  [libx264 @ 0x16e03c0] kb/s:1085.42
  
  [libx264 @ 0x16cc880] frame I:4     Avg QP:15.79  size:181630
  
  [libx264 @ 0x16cc880] frame P:132   Avg QP:18.21  size: 32848
  
  [libx264 @ 0x16cc880] mb I  I16..4: 13.7%  0.0% 86.3%
  
  [libx264 @ 0x16cc880] mb P  I16..4:  1.3%  0.0%  2.7%  P16..4: 36.0% 14.2%  4.4%  0.0%  0.0%    skip:41.4%
  
  [libx264 @ 0x16cc880] coded y,uvDC,uvAC intra: 72.4% 70.6% 41.8% inter: 17.3% 24.9% 1.5%
  
  [libx264 @ 0x16cc880] i16 v,h,dc,p: 33% 21%  6% 39%
  
  [libx264 @ 0x16cc880] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 30% 23%  9%  5%  8%  7%  7%  6%  5%
  
  [libx264 @ 0x16cc880] i8c dc,h,v,p: 44% 21% 27%  8%
  
  [libx264 @ 0x16cc880] ref P L0: 80.3% 12.7%  7.0%
  
  [libx264 @ 0x16cc880] kb/s:3420.59