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• 9 Ways to Customise Your Matomo Like a Pro

  5 octobre 2022, par Erin

  Matomo is a feature-rich web analytics platform. As such, it has many layers of depth — core features, extra plug-ins, custom dimensions, reports, extensions and integrations. 

  Most of the product elements you see can be personalised and customised to your needs with minimal restrictions. However, this breadth of choice can be overlooked by new users. 

  In this post, we explain how to get the most out of Matomo with custom reports, dashboards, dimensions and even app design. 

  How to customise your Matomo web analytics

  To make major changes to Matomo (e.g., create custom dashboards or install new plugins), you’ll have to be a Matomo Super User (a.k.a. The Admin). Super Users can also grant administrator permissions to others so that more people could customise your Matomo deployment. 

  Most feature-related customisations (e.g. configuring a custom report, adding custom goal tracking, etc.) can be done by all users. 

  With the above in mind, here’s how you can tweak Matomo to better serve your website analytics needs : 

  1. Custom dashboards

  

  Dashboards provide a panorama view of all the collected website statistics. We display different categories of stats and KPIs as separate widgets — a standalone module you can also customise. 

  On your dashboard, you can change the type, position and number of widgets on display. This is an easy way to create separate dashboard views for different projects, clients or team members. Rather than a one-size-fits-all dashboard, a custom dashboard designed for a specific role or business unit will increase data-driven decision-making and efficiency across the business.

  You can create a new dashboard view in a few clicks. Then select a preferred layout — a split-page view or multi columns. Next, populate the new dashboard area with preferred widgets showing :

  • SEO website rankings 
  • Visits, conversions and revenue 
  • Custom reports 

  Or code a custom widget area to pull specific website stats or other reporting data you need. Once you are done, arrange everything with our drag-and-drop functionality. 

  

  Popular feature use cases

  • Personalised website statistics layout for convenient viewing 
  • Simplified analytics dashboards for the line of business leaders/stakeholders 
  • Project- or client-specific dashboards for easy report sharing 

  Read more about customising Matomo dashboards and widget areas. 

  2. Custom reports

  

  As the name implies, Custom Reports widget allows you to mesh any of the dimensions and metrics collected by Matomo into a custom website traffic analysis. Custom reports save users time by providing specific data needed in one view so there is no need to jump back and forth between multiple reports or toggle through a report to find data.

  For each custom report, you can select up to three dimensions and then apply additional quantitative measurements (metrics) to drill down into the data.

  For example, if you want to closely observe mobile conversion rates in one market, you can create the following custom report :

  • Dimensions : User Type (registered), Device type (mobile), Location (France)
  • Metrics : Visits, Conversion Rate, Revenue, Avg. Generation Time.

  Custom Report widget is available within Matomo Cloud and as a plugin for Matomo On-Premise.

  &lt;script type=&quot;text/javascript&quot;&gt;<br />
       if ('function' === typeof window.playMatomoVideo){<br />
       window.playMatomoVideo(&quot;custom_reports&quot;, &quot;#custom_reports&quot;)<br />
       } else {<br />
       document.addEventListener(&quot;DOMContentLoaded&quot;, function() { window.playMatomoVideo(&quot;custom_reports&quot;, &quot;#custom_reports&quot;); });<br />
       }<br />
  &lt;/script&gt;

  Popular feature use cases

  • Campaign-specific reporting to better understand the impact of different promo strategies 
  • Advanced event tracking for conversion optimization 
  • Market segmentation reports to analyse different audience cohorts 

  Read more about creating and analysing Custom Reports.

  3. Custom widgets

  

  We realise that our users have different degrees of analytics knowledge. Some love in-depth reporting dimensions and multi-row reporting tables. Others just want to see essential stats. 

  To delight both the pros and the novice users, we’ve created widgets — reporting sub-modules you can add, delete or rearrange in a few clicks. Essentially, a widget is a slice of a dashboard area you can populate with extra information. 

  You can add pre-made custom widgets to Matomo or develop your own widget to display custom reports or even external data (e.g., offline sales volume). At the same time, you can also embed Matomo widgets into other applications (e.g., a website CMS or corporate portal).

  Popular feature use cases

  • Display main goals (e.g., new trial sign-ups) on the main dashboard for greater visibility 
  • Highlight cost-per-conversion reporting by combining goals and conversion data to keep your budgets in check 
  • Run omnichannel eCommerce analytics (with embedded offline sales data) to get a 360-degree view into your operations 

  Read more about creating widgets in Matomo (beginner’s guide). 

  4. Custom dimensions 

  

  Dimensions describe the characteristics of reported data. Think of them as “filters” — a means to organise website analytics data by a certain parameter such as “Browser”, “Country”, “Device Type”, “User Type” and many more. 

  Custom Dimensions come in handy for all sorts of segmentation reports. For example, comparing conversion rates between registered and guest users. Or tracking revenue by device type and location. 

  For convenience, we’ve grouped Custom Dimensions in two categories :

  Visit dimensions. These associate metadata about a user with Visitor profiles — a summary of different knowledge you have about your audience. Reports for Visit scoped custom dimensions are available in the Visitors section of your dashboard. 

  Action dimensions. These segment users by specific actions tracked by Matomo such as pageviews, events completion, downloads, form clicks, etc. When configuring Custom Dimensions, you can select among pre-defined action types or code extra action dimensions. Action scoped custom dimensions are available in the Behaviours section of Matomo. 

  Depending on your Matomo version, you can apply 5 – 15 custom dimensions to reports. 

  Important : Since you can’t delete dimensions (only deactivate them), think about your use case first. Custom Dimensions each have their own dedicated reports page on your Matomo dashboard. 

  Popular custom dimension use cases among users :

  • Segmenting reports by users’ screen resolution size to understand how your website performs on different devices
  • Monitor conversion rates for different page types to determine your best-performing assets 

  Read more about creating, tracking and managing Custom Dimensions. 

  5. Custom scheduled reports

  Manually sending reports can be time consuming, especially if you have multiple clients or provide reports to numerous stakeholders. Custom scheduled reports remove this manual process to improve efficiency and ensure timely distribution of data to relevant users.

  Any report in Matomo (default or custom) can be shared with others by email as a PDF file, HTML content or as an attached CSV document. 

  You can customise which data you want to send to different people — your colleagues, upper management, clients or other company divisions. Then set up the frequency of email dispatches and Matomo will do the rest. 

  Auto-scheduling an email report is easy. Name your report, select a Segment (aka custom or standard report), pick time, file format and sender. 

  

  You can also share links to Matomo reports as text messages, if you are using ASPSMS or Clockwork SMS. 

  Popular feature use cases

  • Convenient stakeholder reporting on key website KPIs 
  • Automated client updates to keep clients informed and reduce workload 
  • Easy data downloads for doing custom analysis with business intelligence tools 

  Read more about email reporting features in Matomo. 

  6. Custom alerts

  

  Custom Alerts is a Matomo plugin for keeping you updated on the most important analytics events. Unlike Custom Reports, which provide a complete or segmented analytics snapshot, alerts are better suited for tracking individual events. For example, significant traffic increases from a specific channel, new 404 pages or major goal achievement (e.g., hitting 1,000 sales in a week). 

  Custom Alerts are a convenient way to keep your finger on the pulse of your site so you can quickly remedy an issue or get updated on reaching a crucial KPI promptly. You can receive custom alerts via email or text message in a matter of minutes.

  To avoid flooding your inbox with alerts, we recommend reserving Custom Alerts for a select few use cases (3 to 5) and schedule custom Email Reports to receive general web page analytics. 

  Popular custom alerts use cases among users :

  • Monitor sudden drops in revenue to investigate the cause behind them and solve any issues promptly 
  • Get notified of traffic spikes or sudden dips to better manage your website’s technical performance 

  Read more about creating and managing Custom Alerts. 

  7. Goals

  

  Goals feature helps you better understand how your website performs on certain business objectives such as lead generation, online sales or content discovery. A goal is a quantifiable action you want to measure (e.g., a specific page visit, form submission or a file download). 

  When combined together, Goals make up your sales funnel — a series of specific actions you expect users to complete in order to convert. 

  Goals-setting and Funnel Analytics are a powerful, customisable combo for understanding how people navigate your website ; what makes them take action or, on the contrary, lose interest and bounce off. 

  On Matomo, you can simultaneously track multiple goals, monitor multiple conversions per one visit (e.g., when one user requests two content downloads) and assign revenue targets to specific goals.

  &lt;script type=&quot;text/javascript&quot;&gt;<br />
       if ('function' === typeof window.playMatomoVideo){<br />
       window.playMatomoVideo(&quot;goals&quot;, &quot;#goals&quot;)<br />
       } else {<br />
       document.addEventListener(&quot;DOMContentLoaded&quot;, function() { window.playMatomoVideo(&quot;goals&quot;, &quot;#goals&quot;); });<br />
       }<br />
  &lt;/script&gt;

  Separately, Matomo Cloud users also get access to a premium Funnels feature and Multi Channel Conversion Attribution. On-Premises Matomo users can get both as paid plugins via our Marketplace.

  Popular goal tracking use cases among users :

  • Tracking newsletter subscription to maximise subscriber growth 
  • Conversion tracking for gated content (e.g., eBooks) to understand how each asset performs 
  • Analysing the volume of job applications per post to better interpret your HR marketing performance 

  Read more about creating and managing Goals in Matomo.

  8. Themes

  

  Want to give your Matomo app a distinctive visual flair ? Pick a new free theme for your On-Premises installation. Minimalistic, dark or classic — our community created six different looks that other Matomo users can download and install in a few clicks. 

  If you have some HTML/CSS/JS knowledge, you can also design your own Matomo theme. Since Matomo is an open-source project, we don’t restrict interface customisation and always welcome creativity from our users.

  Read more about designing your own Matomo theme (developer documentation).

  9. White labelling

  

  Matomo is one of the few website analytics tools to support white labelling. White labelling means that you can distribute our product to others under your brand. 

  For example, as a web design agency, you can delight customers with pre-installed GDPR-friendly website analytics. Marketing services providers, in turn, can present their clients with embedded reporting widgets, robust funnel analytics and 100% unsampled data. 

  Apart from selecting a custom theme, you can also align Matomo with your brand by :

  • Customising product name
  • Using custom header/font colours 
  • Change your tracking endpoint
  • Remove links to Matomo.org

  To streamline Matomo customisation and set-up, we developed a White Label plug-in. It provides a convenient set of controls for changing your Matomo deployment and distributing access rights to other users or sharing embedded Matomo widgets). 

  Read more about white labelling Matomo. 

  Learning more about Matomo 

  Matomo has an ever-growing list of features, ranging from standard website tracking controls to unique conversion rate optimisation tools (heatmaps, media analytics, user cohorts and more).

  To learn more about Matomo features you can check our free video web analytics training series where we cover the basics. For feature-specific tips, tricks and configurations, browse our video content or written guides. 

  21 day free trial. No credit card required.

• Google Analytics Privacy Issues : Is It Really That Bad ?

  2 juin 2022, par Erin

  If you find yourself asking : “What’s the deal with Google Analytics privacy ?”, you probably have some second thoughts. 

  Your hunch is right. Google Analytics (GA) is a popular web analytics tool, but it’s far from being perfect when it comes to respecting users’ privacy. 

  This post helps you understand tremendous Google Analytics privacy concerns users, consumers and regulators expressed over the years.

  In this blog, we’ll cover :

  What Does Google Analytics Collect About Users ? 

  To understand Google Analytics privacy issues, you need to know how Google treats web users’ data. 

  By default, Google Analytics collects the following information : 

  • Session statistics — duration, page(s) viewed, etc. 
  • Referring website details — a link you came through or keyword used. 
  • Approximate geolocation — country, city. 
  • Browser and device information — mobile vs desktop, OS usage, etc. 

  Google obtains web analytics data about users via two means : an on-site Google Analytics tracking code and cookies.

  A cookie is a unique identifier (ID) assigned to each user visiting a web property. Each cookie stores two data items : unique user ID and website name. 

  With the help of cookies, web analytics solutions can recognise returning visitors and track their actions across the website(s).

  

  • First party cookies are generated by one website and collect user behaviour data from said website only. 
  • Third-party cookies are generated by a third-party website object (for example, an ad) and can track user behaviour data across multiple websites. 

  As it’s easy to imagine, third-party cookies are a goldmine for companies selling online ads. Essentially, they allow ad platforms to continue watching how the user navigates the web after clicking a certain link. 

  Yet, people have little clue as to which data they are sharing and how it is being used. Also, user consent to tracking across websites is only marginally guaranteed by existing Google Analytics controls. 

  Why Third-Party Cookie Data Collection By GA Is Problematic 

  Cookies can transmit personally identifiable information (PII) such as name, log in details, IP address, saved payment method and so on. Some of these details can end up with advertisers without consumers’ direct knowledge or consent.

  Regulatory frameworks such as General Data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act (CCPA) emerged as a response to uncontrolled user behaviour tracking.

  Under regulatory pressure, Big Tech companies had to adapt their data collection process.

  Apple was the first to implement by-default third-party blocking in the Safari browser. Then added a tracking consent mechanism for iPhone users starting from iOS 15.2 and later. 

  Google, too, said it would drop third-party cookie usage after The European Commission and UK’s Competition and Markets Authority (CMA) launched antitrust investigations into its activity. 

  To shake off the data watchdogs, Google released a Privacy Sandbox — a set of progressive tech, operational and compliance changes for ensuring greater consumer privacy. 

  Google’s biggest promise : deprecate third-party cookies usage for all web and mobile products. 

  Originally, Google promised to drop third-party cookies by 2022, but that didn’t happen. Instead, Google delayed cookie tracking depreciation for Chrome until the second half of 2023. 

  Why did they push back on this despite hefty fines from regulators ?

  Because online ads make Google a lot of money.

  In 2021, Alphabet Inc (parent company of Google), made $256.7 billion in revenue, of which $209.49 billion came from selling advertising. 

  Lax Google Analytics privacy enforcement — and its wide usage by website owners — help Google make those billions from collecting and selling user data. 

  How Google Uses Collected Google Analytics Data for Advertising 

  Over 28 million websites (or roughly 85% of the Internet) have Google Analytics tracking codes installed. 

  Even if one day we get a Google Analytics version without cookies, it still won’t address all the privacy concerns regulators and consumers have. 

  Over the years, Google has accumulated an extensive collection of user data. The company’s engineers used it to build state-of-the-art deep learning models, now employed to build advanced user profiles. 

  Deep learning is the process of training a machine to recognise data patterns. Then this “knowledge” is used to produce highly-accurate predictive insights. The more data you have for model training — the better its future accuracy will be. 

  Google has amassed huge deposits of data from its collection of products — GA, YouTube, Gmail, Google Docs and Google Maps among others. Now they are using this data to build a third-party cookies-less alternative mechanism for modelling people’s preferences, habits, lifestyles, etc. 

  Their latest model is called Google Topics. 

  This comes only after Google’s failed attempt to replace cookie-based training with Federated Learning of Cohorts (FLoC) model. But the solution wasn’t offering enough user transparency and user controls among other issues.

  

  Google Topics promises to limit the granularity of data advertisers get about users. 

  But it’s still a web user surveillance method. With Google Topics, the company will continue collecting user data via Chrome (and likely other Google products) — and share it with advertisers. 

  Because as we said before : Google is in the business of profiting off consumers’ data. 

  Two Major Ways Google Takes Advantage of Customer Data

  Every bit of data Google collects across its ecosystem of products can be used in two ways :

  • For ad targeting and personalisation 
  • To improve Google’s products 

  The latter also helps the former. 

  Advanced Ad Personalisation and Targeting

  GA provides the company with ample data on users’ 

  • Recent and frequent searches 
  • Location history
  • Visited websites
  • Used apps 
  • Videos and ads viewed 
  • Personal data like age or gender 

  The company’s privacy policy explicitly states that :
  

  

  Google also admits to using collected data to “measure the effectiveness of advertising” and “personalise content and ads you see on Google.” 

  But there are no further elaborations on how exactly customers’ data is used — and what you can do to prevent it from being shared with third parties. 

  In some cases, Google also “forgets” to inform users about its in-product tracking.

  Journalists from CNBC and The New York Times independently concluded that Google monitors users’ Gmail activity. In particular, the company scans your inbox for recent purchases, trips, flights and bills notifications. 

  While Google says that this information isn’t sold to advertisers (directly), they still may use the “saved information about your orders in other Google services”. 

  Once again, this means you have little control or knowledge of subsequent data usage. 

  Improving Product Usability 

  Google has many “arms” to collect different data points — from user’s search history to frequently-travelled physical routes. 

  They also reserve the right to use these insights for improving existing products. 

  Here’s what it means : by combining different types of data points obtained from various products, Google can pierce a detailed picture of a person’s life. Even if such user profile data is anonymised, it is still alarmingly accurate. 

  Douglas Schmidt, a computer science researcher at Vanderbilt University, well summarised the matter : 

  “[Google’s] business model is to collect as much data about you as possible and cross-correlate it so they can try to link your online persona with your offline persona. This tracking is just absolutely essential to their business. ‘Surveillance capitalism’ is a perfect phrase for it.”

  Google Data Collection Obsession Is Backed Into Its Business Model 

  OK, but Google offers some privacy controls to users ? Yes. Google only sees and uses the information you voluntarily enter or permit them to access. 

  But as the Washington Post correspondent points out :

  “[Big Tech] companies get to set all the rules, as long as they run those rules by consumers in convoluted terms of service that even those capable of decoding the legalistic language rarely bother to read. Other mechanisms for notice and consent, such as opt-outs and opt-ins, create similar problems. Control for the consumer is mostly an illusion.”

  Google openly claims to be “one of many ad networks that personalise ads based on your activity online”. 

  The wrinkle is that they have more data than all other advertising networks (arguably combined). This helps Google sell high-precision targeting and contextually personalised ads for billions of dollars annually.

  Given that Google has stakes in so many products — it’s really hard to de-Google your business and minimise tracking and data collection from the company.
  

  They are also creating a monopoly on data collection and ownership. This fact makes regulators concerned. The 2021 antitrust lawsuit from the European Commission says : 

  “The formal investigation will notably examine whether Google is distorting competition by restricting access by third parties to user data for advertising purposes on websites and apps while reserving such data for its own use.”

  In other words : By using consumer data to its unfair advantage, Google allegedly shuts off competition.

  But that’s not the only matter worrying regulators and consumers alike. Over the years, Google also received numerous other lawsuits for breaching people’s privacy, over and over again. 

  Here’s a timeline : 

  • 2019 : UK citizens issued a class action suit against Google for imposing cookies to override users’ privacy settings in the Safari browser. 
  • 2020 : US citizens pushed for a $5 billion class-action suit for tracking their activity through browsers set in “private” mode.
  • 2022 : Another class-action lawsuit in the US for deceptive privacy controls and unconsented location data tracking by Google mobile apps. 
  • 2022 : Google reached a $100 million class-action settlement for breaching Illinois biometrics privacy laws in Google Photos. 

  Separately, Google has a very complex history with GDPR compliance. 

  How Google Analytics Contributes to the Web Privacy Problem 

  Google Analytics is the key puzzle piece that supports Google’s data-driven business model. 

  If Google was to release a privacy-focused Google Analytics alternative, it’d lose access to valuable web users’ data and a big portion of digital ad revenues. 

  Remember : Google collects more data than it shares with web analytics users and advertisers. But they keep a lot of it for personal usage — and keep looking for ways to share this intel with advertisers (in a way that keeps regulators off their tail).

  For Google Analytics to become truly ethical and privacy-focused, Google would need to change their entire revenue model — which is something they are unlikely to do.

  Where does this leave Google Analytics users ? 

  In a slippery territory. By proxy, companies using GA are complicit with Google’s shady data collection and usage practice. They become part of the problem.

  In fact, Google Analytics usage opens a business to two types of risks : 

  • Reputational. 77% of global consumers say that transparency around how data is collected and used is important to them when interacting with different brands. That’s why data breaches and data misuse by brands lead to major public outrages on social media and boycotts in some cases. 
  • Legal. EU regulators are on a continuous crusade against Google Analytics 4 (GA4) as it is in breach of GDPR. French and Austrian watchdogs ruled the “service” illegal. Since Google Analytics is not GDPR compliant, it opens any business using it to lawsuits (which is already happening).

  But there’s a way out.

  Choose a Privacy-Friendly Google Analytics Alternative 

  Google Analytics is a popular web analytics service, but not the only one available. You have alternatives such as Matomo. 

  Our guiding principle is : respecting privacy.

  Unlike Google Analytics, we leave data ownership 100% in users’ hands. Matomo lets you implement privacy-centred controls for user data collection.

  Plus, you can self-host Matomo On-Premise or choose Matomo Cloud with data securely stored in the EU and in compliance with GDPR.

  The best part ? You can try our ethical alternative to Google Analytics for free. No credit card required ! Start your free 21-day trial now. 

  21 day free trial. No credit card required.

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  Choose your analytics subdomain
  

  .matomo.cloud

  I hereby accept the terms and conditions and the data processing agreeement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now

  

• Transcoding hevc to h264 using GPU and scale_npp

  19 mai 2022, par JanZg

  I am trying to transcode hevc to h264 using GPU. Default input is 10 bit hevc but I also try it on 8 bit hevc. I use hevc_cuvid decoder and h264_nvenc encoder. While scaling using CPU its correct. I was also using scale_npp=w=1280:h=720:format=yuv420p and scale_cuda - > no result.

  


  Working command without using GPU to scale :

  


  /usr/local/bin/ffmpeg -y -v warning -c:v hevc_cuvid -an -sn -dn -reconnect_at_eof 1 -reconnect_streamed 1 -reconnect_on_network_error 1 -i http://1.2.3.4:8794 -c:v h264_nvenc -pix_fmt yuv420p -vf scale=1280:720 test_hevc.mp4


  


  But when I try to scale using GPU for example scale_cuda or scale_npp I get this error :

  


  Command :

  


  /usr/local/bin/ffmpeg -y -v warning -c:v hevc_cuvid -an -sn -dn -reconnect_at_eof 1 -reconnect_streamed 1 -reconnect_on_network_error 1 -i http://1.2.3.4:8794 -c:v h264_nvenc -pix_fmt yuv420p -vf scale_npp=1280:720 test_hevc.mp4


  


  Outputs :

  


  Error output with trace log level

  


  [graph 0 input from stream 0:0 @ 0x56164efb6b40] w:3840 h:2160 pixfmt:p010le tb:1/90000 fr:50/1 sar:1/1
[format @ 0x56164efb7940] Setting ‘pix_fmts’ to value ‘yuv420p’
[auto_scale_0 @ 0x56164efb9940] w:iw h:ih flags:’’ interl:0
[Parsed_scale_npp_0 @ 0x56164efb58c0] auto-inserting filter ‘auto_scale_0’ between the filter ‘graph 0 input from stream 0:0’ and the filter ‘Parsed_scale_npp_0’
Impossible to convert between the formats supported by the filter ‘graph 0 input from stream 0:0’ and the filter ‘auto_scale_0’
Error reinitializing filters!
Failed to inject frame into filter network: Function not implemented
Error while processing the decoded data for stream #0:0
[AVIOContext @ 0x56164ea41300] Statistics: 0 seeks, 0 writeouts
[AVIOContext @ 0x56164ea39c80] Statistics: 15357720 bytes read, 0 seeks
[AVHWDeviceContext @ 0x56164ea68480] Calling cu->cuCtxDestroy(hwctx->cuda_ctx)
Conversion failed!


  


  Full output with warning log level

  


  [hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[hevc @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 1 times
[hevc @ 0x555bd6e9f0c0] Error parsing NAL unit #2.
[mpegts @ 0x555bd6e85dc0] Could not find codec parameters for stream 0 (Video: hevc (HEVC / 0x43564548), none): unspecified size
Consider increasing the value for the ‘analyzeduration’ (0) and ‘probesize’ (5000000) options
[NULL @ 0x555bd6e9f0c0] PPS id out of range: 0
Last message repeated 66 times
Impossible to convert between the formats supported by the filter ‘graph 0 input from stream 0:0’ and the filter ‘auto_scale_0’
Error reinitializing filters!
Failed to inject frame into filter network: Function not implemented
Error while processing the decoded data for stream #0:0

Additional informations:

My FFmpeg Version below:

ffmpeg version N-103630-g06de593303 Copyright (c) 2000-2021 the FFmpeg developers
built with gcc 8 (Debian 8.3.0-6)
configuration: --prefix=/usr/local --enable-libtwolame --enable-libzvbi --enable-nonfree --enable-cuda-nvcc --nvccflags=’-gencode arch=compute_75,code=sm_75 -O2’ --enable-libnpp --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64
libavutil 57. 5.100 / 57. 5.100
libavcodec 59. 7.103 / 59. 7.103
libavformat 59. 5.100 / 59. 5.100
libavdevice 59. 0.101 / 59. 0.101
libavfilter 8. 9.100 / 8. 9.100
libswscale 6. 1.100 / 6. 1.100
libswresample 4. 0.100 / 4. 0.100


  


  GPU : Tesla T4

  


  System : Linux tgpu 4.19.0-17-amd64 #1 SMP Debian 4.19.194-3 (2021-07-18) x86_64 GNU/Linux

  


  Nvcc version : nvcc : NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Oct_12_20:09:46_PDT_2020
Cuda compilation tools, release 11.1, V11.1.105
Build cuda_11.1.TC455_06.29190527_0

  


  Is it possible to scale hevc using GPU and how to fix this ?
Thank You in advance.