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• Should H.264 bit rate be multiples of 8 ?

  31 août 2016, par Dan Sharp

  I’m working on a video platform receiving H.264 video and building an HLS stream (transmuxing the H.264 to Mpeg2 TS segments via calls to ffmpeg).

  I wanted to set the bit rate to be about 2000 kbps, but I’m wondering : does it matter if it’s 2000 or 2048 ?

  In other words, do things calculate better if the bit rate is multiples of 8, like 512 or 2024 or 2048 ?

  I don’t know enough about how the bit rate is used, either on the sending side (camera) or on the processing side (ffmpeg).

  From tests... I can’t see any noticeable difference between 2000 and 2048, but maybe one is slightly better than another for the transmuxing and segmenting ?

  I welcome any thoughts/advice.

• swscale/x86/input.asm : add x86-optimized planer rgb2yuv functions

  24 novembre 2021, par Mark Reid

  swscale/x86/input.asm : add x86-optimized planer rgb2yuv functions
  sse2 only operates on 2 lanes per loop for to_y and to_uv functions, due
  
  to the lack of pmulld instruction.  Emulating pmulld with 2 pmuludq and shuffles
  
  proved too costly and made to_uv functions slower then the c implementation.
  For to_y on sse2 only float functions are generated,
  
  I was are not able outperform the c implementation on the integer pixel formats.
  For to_a on see4 only the float functions are generated.
  
  sse2 and sse4 generated nearly identical performing code on integer pixel formats,
  
  so only sse2/avx2 versions are generated.
  planar_gbrp_to_y_512_c : 1197.5
  
  planar_gbrp_to_y_512_sse4 : 444.5
  
  planar_gbrp_to_y_512_avx2 : 287.5
  
  planar_gbrap_to_y_512_c : 1204.5
  
  planar_gbrap_to_y_512_sse4 : 447.5
  
  planar_gbrap_to_y_512_avx2 : 289.5
  
  planar_gbrp9be_to_y_512_c : 1380.0
  
  planar_gbrp9be_to_y_512_sse4 : 543.5
  
  planar_gbrp9be_to_y_512_avx2 : 340.0
  
  planar_gbrp9le_to_y_512_c : 1200.5
  
  planar_gbrp9le_to_y_512_sse4 : 442.0
  
  planar_gbrp9le_to_y_512_avx2 : 282.0
  
  planar_gbrp10be_to_y_512_c : 1378.5
  
  planar_gbrp10be_to_y_512_sse4 : 544.0
  
  planar_gbrp10be_to_y_512_avx2 : 337.5
  
  planar_gbrp10le_to_y_512_c : 1200.0
  
  planar_gbrp10le_to_y_512_sse4 : 448.0
  
  planar_gbrp10le_to_y_512_avx2 : 285.5
  
  planar_gbrap10be_to_y_512_c : 1380.0
  
  planar_gbrap10be_to_y_512_sse4 : 542.0
  
  planar_gbrap10be_to_y_512_avx2 : 340.5
  
  planar_gbrap10le_to_y_512_c : 1199.0
  
  planar_gbrap10le_to_y_512_sse4 : 446.0
  
  planar_gbrap10le_to_y_512_avx2 : 289.5
  
  planar_gbrp12be_to_y_512_c : 10563.0
  
  planar_gbrp12be_to_y_512_sse4 : 542.5
  
  planar_gbrp12be_to_y_512_avx2 : 339.0
  
  planar_gbrp12le_to_y_512_c : 1201.0
  
  planar_gbrp12le_to_y_512_sse4 : 440.5
  
  planar_gbrp12le_to_y_512_avx2 : 286.0
  
  planar_gbrap12be_to_y_512_c : 1701.5
  
  planar_gbrap12be_to_y_512_sse4 : 917.0
  
  planar_gbrap12be_to_y_512_avx2 : 338.5
  
  planar_gbrap12le_to_y_512_c : 1201.0
  
  planar_gbrap12le_to_y_512_sse4 : 444.5
  
  planar_gbrap12le_to_y_512_avx2 : 288.0
  
  planar_gbrp14be_to_y_512_c : 1370.5
  
  planar_gbrp14be_to_y_512_sse4 : 545.0
  
  planar_gbrp14be_to_y_512_avx2 : 338.5
  
  planar_gbrp14le_to_y_512_c : 1199.0
  
  planar_gbrp14le_to_y_512_sse4 : 444.0
  
  planar_gbrp14le_to_y_512_avx2 : 279.5
  
  planar_gbrp16be_to_y_512_c : 1364.0
  
  planar_gbrp16be_to_y_512_sse4 : 544.5
  
  planar_gbrp16be_to_y_512_avx2 : 339.5
  
  planar_gbrp16le_to_y_512_c : 1201.0
  
  planar_gbrp16le_to_y_512_sse4 : 445.5
  
  planar_gbrp16le_to_y_512_avx2 : 280.5
  
  planar_gbrap16be_to_y_512_c : 1377.0
  
  planar_gbrap16be_to_y_512_sse4 : 545.0
  
  planar_gbrap16be_to_y_512_avx2 : 338.5
  
  planar_gbrap16le_to_y_512_c : 1201.0
  
  planar_gbrap16le_to_y_512_sse4 : 442.0
  
  planar_gbrap16le_to_y_512_avx2 : 279.0
  
  planar_gbrpf32be_to_y_512_c : 4113.0
  
  planar_gbrpf32be_to_y_512_sse2 : 2438.0
  
  planar_gbrpf32be_to_y_512_sse4 : 1068.0
  
  planar_gbrpf32be_to_y_512_avx2 : 904.5
  
  planar_gbrpf32le_to_y_512_c : 3818.5
  
  planar_gbrpf32le_to_y_512_sse2 : 2024.5
  
  planar_gbrpf32le_to_y_512_sse4 : 1241.5
  
  planar_gbrpf32le_to_y_512_avx2 : 657.0
  
  planar_gbrapf32be_to_y_512_c : 3707.0
  
  planar_gbrapf32be_to_y_512_sse2 : 2444.0
  
  planar_gbrapf32be_to_y_512_sse4 : 1077.0
  
  planar_gbrapf32be_to_y_512_avx2 : 909.0
  
  planar_gbrapf32le_to_y_512_c : 3822.0
  
  planar_gbrapf32le_to_y_512_sse2 : 2024.5
  
  planar_gbrapf32le_to_y_512_sse4 : 1176.0
  
  planar_gbrapf32le_to_y_512_avx2 : 658.5
  planar_gbrp_to_uv_512_c : 2325.8
  
  planar_gbrp_to_uv_512_sse2 : 1726.8
  
  planar_gbrp_to_uv_512_sse4 : 771.8
  
  planar_gbrp_to_uv_512_avx2 : 506.8
  
  planar_gbrap_to_uv_512_c : 2281.8
  
  planar_gbrap_to_uv_512_sse2 : 1726.3
  
  planar_gbrap_to_uv_512_sse4 : 768.3
  
  planar_gbrap_to_uv_512_avx2 : 496.3
  
  planar_gbrp9be_to_uv_512_c : 2336.8
  
  planar_gbrp9be_to_uv_512_sse2 : 1924.8
  
  planar_gbrp9be_to_uv_512_sse4 : 852.3
  
  planar_gbrp9be_to_uv_512_avx2 : 552.8
  
  planar_gbrp9le_to_uv_512_c : 2270.3
  
  planar_gbrp9le_to_uv_512_sse2 : 1512.3
  
  planar_gbrp9le_to_uv_512_sse4 : 764.3
  
  planar_gbrp9le_to_uv_512_avx2 : 491.3
  
  planar_gbrp10be_to_uv_512_c : 2281.8
  
  planar_gbrp10be_to_uv_512_sse2 : 1917.8
  
  planar_gbrp10be_to_uv_512_sse4 : 855.3
  
  planar_gbrp10be_to_uv_512_avx2 : 541.3
  
  planar_gbrp10le_to_uv_512_c : 2269.8
  
  planar_gbrp10le_to_uv_512_sse2 : 1515.3
  
  planar_gbrp10le_to_uv_512_sse4 : 759.8
  
  planar_gbrp10le_to_uv_512_avx2 : 487.8
  
  planar_gbrap10be_to_uv_512_c : 2382.3
  
  planar_gbrap10be_to_uv_512_sse2 : 1924.8
  
  planar_gbrap10be_to_uv_512_sse4 : 855.3
  
  planar_gbrap10be_to_uv_512_avx2 : 540.8
  
  planar_gbrap10le_to_uv_512_c : 2382.3
  
  planar_gbrap10le_to_uv_512_sse2 : 1512.3
  
  planar_gbrap10le_to_uv_512_sse4 : 759.3
  
  planar_gbrap10le_to_uv_512_avx2 : 484.8
  
  planar_gbrp12be_to_uv_512_c : 2283.8
  
  planar_gbrp12be_to_uv_512_sse2 : 1936.8
  
  planar_gbrp12be_to_uv_512_sse4 : 858.3
  
  planar_gbrp12be_to_uv_512_avx2 : 541.3
  
  planar_gbrp12le_to_uv_512_c : 2278.8
  
  planar_gbrp12le_to_uv_512_sse2 : 1507.3
  
  planar_gbrp12le_to_uv_512_sse4 : 760.3
  
  planar_gbrp12le_to_uv_512_avx2 : 485.8
  
  planar_gbrap12be_to_uv_512_c : 2385.3
  
  planar_gbrap12be_to_uv_512_sse2 : 1927.8
  
  planar_gbrap12be_to_uv_512_sse4 : 855.3
  
  planar_gbrap12be_to_uv_512_avx2 : 539.8
  
  planar_gbrap12le_to_uv_512_c : 2377.3
  
  planar_gbrap12le_to_uv_512_sse2 : 1516.3
  
  planar_gbrap12le_to_uv_512_sse4 : 759.3
  
  planar_gbrap12le_to_uv_512_avx2 : 484.8
  
  planar_gbrp14be_to_uv_512_c : 2283.8
  
  planar_gbrp14be_to_uv_512_sse2 : 1935.3
  
  planar_gbrp14be_to_uv_512_sse4 : 852.3
  
  planar_gbrp14be_to_uv_512_avx2 : 540.3
  
  planar_gbrp14le_to_uv_512_c : 2276.8
  
  planar_gbrp14le_to_uv_512_sse2 : 1514.8
  
  planar_gbrp14le_to_uv_512_sse4 : 762.3
  
  planar_gbrp14le_to_uv_512_avx2 : 484.8
  
  planar_gbrp16be_to_uv_512_c : 2383.3
  
  planar_gbrp16be_to_uv_512_sse2 : 1881.8
  
  planar_gbrp16be_to_uv_512_sse4 : 852.3
  
  planar_gbrp16be_to_uv_512_avx2 : 541.8
  
  planar_gbrp16le_to_uv_512_c : 2378.3
  
  planar_gbrp16le_to_uv_512_sse2 : 1476.8
  
  planar_gbrp16le_to_uv_512_sse4 : 765.3
  
  planar_gbrp16le_to_uv_512_avx2 : 485.8
  
  planar_gbrap16be_to_uv_512_c : 2382.3
  
  planar_gbrap16be_to_uv_512_sse2 : 1886.3
  
  planar_gbrap16be_to_uv_512_sse4 : 853.8
  
  planar_gbrap16be_to_uv_512_avx2 : 550.8
  
  planar_gbrap16le_to_uv_512_c : 2381.8
  
  planar_gbrap16le_to_uv_512_sse2 : 1488.3
  
  planar_gbrap16le_to_uv_512_sse4 : 765.3
  
  planar_gbrap16le_to_uv_512_avx2 : 491.8
  
  planar_gbrpf32be_to_uv_512_c : 4863.0
  
  planar_gbrpf32be_to_uv_512_sse2 : 3347.5
  
  planar_gbrpf32be_to_uv_512_sse4 : 1800.0
  
  planar_gbrpf32be_to_uv_512_avx2 : 1199.0
  
  planar_gbrpf32le_to_uv_512_c : 4725.0
  
  planar_gbrpf32le_to_uv_512_sse2 : 2753.0
  
  planar_gbrpf32le_to_uv_512_sse4 : 1474.5
  
  planar_gbrpf32le_to_uv_512_avx2 : 927.5
  
  planar_gbrapf32be_to_uv_512_c : 4859.0
  
  planar_gbrapf32be_to_uv_512_sse2 : 3269.0
  
  planar_gbrapf32be_to_uv_512_sse4 : 1802.0
  
  planar_gbrapf32be_to_uv_512_avx2 : 1201.5
  
  planar_gbrapf32le_to_uv_512_c : 6338.0
  
  planar_gbrapf32le_to_uv_512_sse2 : 2756.5
  
  planar_gbrapf32le_to_uv_512_sse4 : 1476.0
  
  planar_gbrapf32le_to_uv_512_avx2 : 908.5
  planar_gbrap_to_a_512_c : 383.3
  
  planar_gbrap_to_a_512_sse2 : 66.8
  
  planar_gbrap_to_a_512_avx2 : 43.8
  
  planar_gbrap10be_to_a_512_c : 601.8
  
  planar_gbrap10be_to_a_512_sse2 : 86.3
  
  planar_gbrap10be_to_a_512_avx2 : 34.8
  
  planar_gbrap10le_to_a_512_c : 602.3
  
  planar_gbrap10le_to_a_512_sse2 : 48.8
  
  planar_gbrap10le_to_a_512_avx2 : 31.3
  
  planar_gbrap12be_to_a_512_c : 601.8
  
  planar_gbrap12be_to_a_512_sse2 : 111.8
  
  planar_gbrap12be_to_a_512_avx2 : 41.3
  
  planar_gbrap12le_to_a_512_c : 385.8
  
  planar_gbrap12le_to_a_512_sse2 : 75.3
  
  planar_gbrap12le_to_a_512_avx2 : 39.8
  
  planar_gbrap16be_to_a_512_c : 386.8
  
  planar_gbrap16be_to_a_512_sse2 : 79.8
  
  planar_gbrap16be_to_a_512_avx2 : 31.3
  
  planar_gbrap16le_to_a_512_c : 600.3
  
  planar_gbrap16le_to_a_512_sse2 : 40.3
  
  planar_gbrap16le_to_a_512_avx2 : 30.3
  
  planar_gbrapf32be_to_a_512_c : 1148.8
  
  planar_gbrapf32be_to_a_512_sse2 : 611.3
  
  planar_gbrapf32be_to_a_512_sse4 : 234.8
  
  planar_gbrapf32be_to_a_512_avx2 : 183.3
  
  planar_gbrapf32le_to_a_512_c : 851.3
  
  planar_gbrapf32le_to_a_512_sse2 : 263.3
  
  planar_gbrapf32le_to_a_512_sse4 : 199.3
  
  planar_gbrapf32le_to_a_512_avx2 : 156.8
  Reviewed-by : Paul B Mahol <onemda@gmail.com>
  
  Signed-off-by : James Almer <jamrial@gmail.com>
  

  • [DH] libswscale/x86/input.asm
  • [DH] libswscale/x86/swscale.c
  • [DH] tests/checkasm/sw_gbrp.c

• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.