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• What is PII ? Your introduction to personally identifiable information

  15 janvier 2020, par Joselyn Khor — Analytics Tips, Privacy, Security
  Most websites you visit collect information about you via tools like Google Analytics and Matomo – sometimes collecting personally identifiable information (PII).

  When it comes to PII, people are becoming more concerned about data privacy. Identifiable information can be used for illegal purposes like identity theft and fraud. 

  So how can you protect yourself as an innocent internet browser ? In the case of website owners – how do you protect users and your company from falling prey to privacy breaches ?

  

  As one of the most trusted analytics companies, we feel our readers would benefit from being as informed as possible about data privacy issues and PII. Learn what it means, and what you can do to keep yours or others’ information safe.

  Table of Contents

  What does PII stand for ?

  PII acronym

  PII is an acronym for personally identifiable information.

  PII definition

  Personally identifiable information (PII) is a term used predominantly in the United States.

  The appendix of OMB M-10-23 (Guidance for Agency Use of Third-Party Website and Applications) gives this definition for PII :

  “The term ‘personally identifiable information’ refers to information which can be used to distinguish or trace an individual’s identity, such as their name, social security number, biometric records, etc. alone, or when combined with other personal or identifying information which is linked or linkable to a specific individual, such as date and place of birth, mother’s maiden name, etc.”

  What can be considered personally identifiable information (PII) ? Some PII examples :

  • Full name/usernames
  • Home address/mailing address
  • Email address
  • Credit card numbers
  • Date of birth
  • Phone numbers
  • Login details

  • Precise locations
  • Account numbers
  • Passwords
  • Security codes (including biometric records)
  • Personal identification numbers
  • Driver license number
  • Get a more comprehensive list here

  What’s non-PII ?

  Anonymous information, or information that can’t be traced back to an individual, can be considered non-PII.

  Who is affected by the exploitation of PII ?

  Anyone can be affected by the exploitation of personal data, where you have identity theft, account fraud and account takeovers. When websites resort to illegally selling or sharing your data and compromising your privacy, the fear is falling victim to such fraudulent activity. 

  PII can also be an issue when employees have access to the database and the data is not encrypted. For example, anyone working in a bank can access your accounts ; anyone working at Facebook may be able to read your messages. This shows how privacy breaches can easily happen when employees have access to PII.

  Website owner’s responsibility for data privacy (PII and analytics)

  To respect your website visitor’s privacy, best practice is to avoid collecting PII whenever possible. If you work in an industry which requires people to disclose personal information (e.g. healthcare, security industries, public sector), then you must ensure this data is collected and handled securely. 

  

  The US National Institute of Standards and Technology states : “The likelihood of harm caused by a breach involving PII is greatly reduced if an organisation minimises the amount of PII it uses, collects, and stores. For example, an organisation should only request PII in a new form if the PII is absolutely necessary.” 

  How you’re held accountable remains up to the privacy laws of the country you’re doing business in. Make sure you are fully aware of the privacy and data protection laws that relate specifically to you. 

  To reduce the risk of privacy breaches, try collecting as little PII as you can ; purging it as soon as you can ; and making sure your IT security is updated and protected against security threats. 

  If you’re using data collection tools like web analytics, data may be tracked through features like User ID, custom variables, and custom dimensions. Sometimes they are also harder to identify when they are present, for example, in page URLs, page titles, or referrers URLs. So make sure you’re optimising your web analytics tools’ settings to ensure you’re asking your users for consent and respecting users’ privacy.

  If you’re using a GDPR compliant tool like Matomo, learn how you can stop processing such personal data

  PII, GDPR and businesses in the US/EU

  Because PII is broad, you may run into confusion when considering PII and GDPR (which applies in the EU). The General Data Protection Regulation (GDPR) provides more safeguards for user privacy.

  GDPR grants people in the EU more rights concerning their “personal data” (more on PII vs personal data below). In the EU the GDPR restricts the collection and processing of personal data. The repercussions are severe penalties and fines for privacy infringements. Businesses are required to handle this personal data carefully. You can be fined up to 4% of their yearly revenue for data breaches or non-compliance. 

  

  Although there isn’t an overarching data protection law in the US, there are hundreds of laws on both the federal and state levels to protect the personal data of US residents. US Congress has also enacted industry-specific statutes related to data privacy, and the state of California passed the California Consumer Privacy Act. 

  To be on the safe side, if you are using analytics, follow matters relating to “personal data” in the GDPR. It’s all-encompassing when it comes to protecting user privacy. GDPR rules still apply whenever an EU citizen visits any non EU site (that processes personal data).

  Personally identifiable information (PII) vs personal data

  PII and “personal data” aren’t used interchangeably. All personal data can be PII, but not all PII can be defined as personal data.

  The definition of “personal data” according to the GDPR :

  

  This means “personal data” encompasses a greater number of identifiers which include the online sphere. Examples include : IP addresses and URL names. As well as seemingly “innocent” data like height, job position, company etc. 

  What’s considered personal data depends on the context. If a piece of information can be combined with others to establish someone’s identity then that can be considered personal data. 

  Under GDPR, when processing personal data, you need explicit consent. You need to ensure you’re compliant according to GDPR definitions of “personal data” not just what’s considered “PII”.

  How Matomo deals with PII and personal data

  Although Matomo Analytics is a web analytics software that tracks user activity on your website, we take privacy and PII very seriously – on both our Cloud and On-Premise offerings. 

  If you’re using Matomo and would like to know how you can be fully GDPR compliant and protect user privacy, read more :
  

  • Learn how to not process any personally identifiable information – Anonymise IP addresses, user IDs, and order IDs
  • Matomo protects user privacy by talking the talk and walking the walk
  • Stay ahead of the GDPR with a privacy-respecting analytics platform
  • 11 ways Matomo helps you protect your visitor’s privacy

  Disclaimer

  We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to issues you may encounter when dealing with PII. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns. 

  Additional resources :

  • https://www.consumeraffairs.com/finance/identity-theft-statistics.html#identity-theft-trends-in-2019
  • https://ec.europa.eu/info/law/law-topic/data-protection_en
  • https://iclg.com/practice-areas/data-protection-laws-and-regulations/usa
  • https://www.csoonline.com/article/3215864/how-to-protect-personally-identifiable-information-pii-under-gdpr.html
  • https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/key-definitions/what-is-personal-data/

• Your introduction to personally identifiable information : What is PII ?

  15 janvier 2020, par Joselyn Khor — Analytics Tips, Privacy, Security
  When it comes to personally identifiable information (PII), people are becoming more concerned with data privacy. Identifiable information can be used for illegal purposes like identity theft and fraud. 

  So how can you protect yourself as an innocent web browser ?

  If you’re a website owner – how do you protect users and your company from falling prey to privacy breaches ?

  As one of the most trusted analytics companies, we feel our readers would benefit from being as informed as possible about data privacy issues and PII. Learn how you can keep yours or others’ information safe.

  

  Table of Contents

  What does PII stand for ?

  PII acronym

  PII is an acronym for personally identifiable information.

  PII definition

  Personally identifiable information (PII) is a term mainly used in the United States.

  The appendix of OMB M-10-23 (Guidance for Agency Use of Third-Party Website and Applications) gives this definition for PII :

  “The term ‘personally identifiable information’ refers to information which can be used to distinguish or trace an individual’s identity, such as their name, social security number, biometric records, etc. alone, or when combined with other personal or identifying information which is linked or linkable to a specific individual, such as date and place of birth, mother’s maiden name, etc.”

  What can be considered personally identifiable information (PII) ? Some PII examples :

  • Full name/usernames
  • Home address/mailing address
  • Email address
  • Credit card numbers
  • Date of birth
  • Phone numbers
  • Login details

  • Precise locations
  • Account numbers
  • Passwords
  • Security codes (including biometric records)
  • Personal identification numbers
  • Driver license number
  • Get a more comprehensive list here

  What’s non-PII ?

  Anonymous information, or information that can’t be traced back to an individual, can be considered non-PII. 

  Who is affected by the exploitation of PII ?

  Anyone can be affected by the misuse of personal data. Websites can compromise your privacy by mishandling or illegally selling/sharing your data. That may lead identity theft, account fraud and account takeovers. The fear is falling victim to such fraudulent activity. 

  PII can also be an issue when employees have access to the database and the data is not encrypted. For example, anyone working in a bank can access your accounts ; and anyone working at Facebook can read your messages. This shows how privacy breaches can easily happen when employees have access to PII.

  Website owner’s responsibility for data privacy (PII and analytics)

  If you’re using a web analytics tool like Google Analytics or Matomo, best practise is to not collect PII if possible. This is to better respect your website visitor’s privacy. 

  If you work in an industry which needs people to share personal information (e.g. healthcare, security industries, public sector), then you must collect and handle this data securely. 

  

  The US National Institute of Standards and Technology states : “The likelihood of harm caused by a breach involving PII is greatly reduced if an organisation minimises the amount of PII it uses, collects, and stores. For example, an organisation should only request PII in a new form if the PII is absolutely necessary.” 

  How you’re held accountable remains up to the privacy laws of the country you’re doing business in. Make sure you are fully aware of the privacy and data protection laws that relate specifically to you. 

  To reduce the risk of privacy breaches, try collecting as little PII as you can ; purging it as soon as you can ; and making sure your IT security is updated and protected against security threats. 

  With data collection tools like web analytics, data may be tracked through features like User ID, custom variables, and custom dimensions. Sometimes they are also harder to identify when they are present, for example, in page URLs, page titles, or referrers URLs. So make sure you’re optimising your web analytics tools’ settings to ensure you’re asking your users for consent and respecting users’ privacy.

  If you’re using a GDPR compliant tool like Matomo, learn how you can stop processing such personal data

  PII, GDPR and businesses in the US/EU

  You may get confused when considering PII and GDPR (which applies in the EU). The General Data Protection Regulation (GDPR) gives people in the EU more rights over “personal data” – which covers more identifiers than PII (more on PII vs personal data below). GDPR restricts the collection and processing of personal data so businesses need to handle this personal data carefully. 

  According to the GDPR, you can be fined up to 4% of their yearly revenue for data/privacy breaches or non-compliance. 

  

  In the US, there isn’t one overarching data protection law, but there are hundreds of laws on both the federal and state levels to protect PII of US residents. US Congress has enacted industry-specific statutes related to data privacy like HIPAA. Recently state of California also passed the California Consumer Privacy Act (CCPA). 

  To be on the safe side, if you’re using analytics, follow matters relating to “personal data” in the GDPR. It covers more when it comes to protecting user privacy. GDPR rules still apply whenever an EU citizen visits any non EU site (that processes personal data).

  Personally identifiable information (PII) vs personal data

  PII and “personal data” aren’t used interchangeably. All personal data can be PII, but not all PII can be defined as personal data.

  The definition of “personal data” according to the GDPR :

  

  This means “personal data” covers more identifiers, including online identifiers. Examples include : IP addresses and URL names. As well as seemingly “innocent” data like height, job position, company etc. 

  What’s seen as personal data depends on the context. If a piece of information can be combined with others to establish someone’s identity then that can be considered personal data. 

  Under GDPR, when processing personal data, you need explicit consent. So best to be compliant according to GDPR definitions of “personal data” not just what’s considered “PII”.

  How do you keep PII safe ?

  • Try not to give your data away so easily. Read through terms and conditions.
  • Don’t just click ‘agree’ when faced with consent screens, as consent screens are majorly flawed. 
  • Disable third party cookies by default. 
  • Use strong passwords.
  • Be wary of public wifi – hackers can easily access your PII or sensitive data. Use a VPN (virtual private network)
  • Read more on how to keep PII safe. For businesses here’s a checklist on PII compliance.

  How Matomo deals with PII and personal data

  Although Matomo Analytics is a web analytics tool that tracks user activity on your website, we take privacy and PII very seriously – on both our Cloud and On-Premise offerings. 

  If you’re using Matomo and would like to know how you can be fully GDPR compliant and protect user privacy, read more :
  

  • Learn how to not process any personally identifiable information – Anonymise IP addresses, user IDs, and order IDs
  • Matomo protects user privacy by talking the talk and walking the walk
  • Stay ahead of the GDPR with a privacy-respecting analytics platform
  • 11 ways Matomo helps you protect your visitor’s privacy

  Disclaimer

  We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to issues you may encounter when dealing with PII. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns. 

  Additional resources :

  • https://www.consumeraffairs.com/finance/identity-theft-statistics.html#identity-theft-trends-in-2019
  • https://ec.europa.eu/info/law/law-topic/data-protection_en
  • https://iclg.com/practice-areas/data-protection-laws-and-regulations/usa
  • https://www.csoonline.com/article/3215864/how-to-protect-personally-identifiable-information-pii-under-gdpr.html
  • https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/key-definitions/what-is-personal-data/

• How to speed up creating video mosaic with ffmpeg

  7 janvier 2020, par DALER RAHIMOV

  I’m looking to speed up ffmpeg pipeline in some way (camera configuration, different filters or any other ideas would be appreciated).

  I have a device that captures videos streams and later creates mosaic video view from 4 cameras. The main issue I’m having is that it’s taking too long to create mosaic video. There is no GPU on the device that could be used to accelerate the process so I’m left with camera configurations (Hikvision).

  Here is what I have so far

  About 160 sec on Intel J-1900 :

  - 5 min video files,
  
  - 640*480 resolution,
  
  - h264 encoding,
  
  - 10 fps,
  
  - 1024 max bitrate,
  
  - 10 I-frame interval,      

  Command that I’m using :

  ffmpeg -y -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 \
  
     -filter_complex " \
  
        color=c=black:size=1280x720 [base]; \
  
        [0:v] setpts=PTS-STARTPTS, scale=640x360 [cam0]; \
  
        [1:v] setpts=PTS-STARTPTS, scale=640x360 [cam1]; \
  
        [2:v] setpts=PTS-STARTPTS, scale=640x360 [cam2]; \
  
        [3:v] setpts=PTS-STARTPTS, scale=640x360 [cam3]; \
  
        [base][cam0] overlay=shortest=1:x=0:y=0  [z1]; \
  
        [z1][cam1] overlay=shortest=1:x=640:y=0  [z2]; \
  
        [z2][cam2] overlay=shortest=1:x=0:y=360  [z3]; \
  
        [z3][cam3] overlay=shortest=1:x=640:y=360 \
  
      " \
  
      -an -c:v libx264  -x264-params keyint=10 \
  
      -movflags faststart -preset fast -nostats -loglevel quiet -r 10.000000 mosaic.mp4

  Thanks

  Here is full output as requested

  ffmpeg -y -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 \
  
  >    -filter_complex " \
  
  >       color=c=black:size=1280x720 [base]; \
  
  >       [0:v] setpts=PTS-STARTPTS, scale=640x360 [cam0]; \
  
  >       [1:v] setpts=PTS-STARTPTS, scale=640x360 [cam1]; \
  
  >       [2:v] setpts=PTS-STARTPTS, scale=640x360 [cam2]; \
  
  >       [3:v] setpts=PTS-STARTPTS, scale=640x360 [cam3]; \
  
  >       [base][cam0] overlay=shortest=1:x=0:y=0  [z1]; \
  
  >       [z1][cam1] overlay=shortest=1:x=640:y=0  [z2]; \
  
  >       [z2][cam2] overlay=shortest=1:x=0:y=360  [z3]; \
  
  >       [z3][cam3] overlay=shortest=1:x=640:y=360 \
  
  >     " \
  
  >     -an -c:v libx264  -x264-params keyint=10 \
  
  >     -movflags faststart -preset fast -nostats -r 10.000000 mosaic.mp4
  
  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 -y -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4 -i 1578324600-1-stitched.mp4    -filter_complex " \
  
        color=c=black:size=1280x720 [base]; \
  
        [0:v] setpts=PTS-STARTPTS, scale=640x360 [cam0]; \
  
        [1:v] setpts=PTS-STARTPTS, scale=640x360 [cam1]; \
  
        [2:v] setpts=PTS-STARTPTS, scale=640x360 [cam2]; \
  
        [3:v] setpts=PTS-STARTPTS, scale=640x360 [cam3]; \
  
        [base][cam0] overlay=shortest=1:x=0:y=0  [z1]; \
  
        [z1][cam1] overlay=shortest=1:x=640:y=0  [z2]; \
  
        [z2][cam2] overlay=shortest=1:x=0:y=360  [z3]; \
  
        [z3][cam3] overlay=shortest=1:x=640:y=360 \
  
      "     -an -c:v libx264  -x264-params keyint=10     -movflags faststart -preset fast -r 10.000000 mosaic.mp4
  
  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
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '1578324600-1-stitched.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.40.101
  
    Duration: 00:05:00.07, start: 0.000000, bitrate: 96 kb/s
  
      Stream #0:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 640x480, 95 kb/s, 10 fps, 25 tbr, 10240 tbn, 20 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
  Input #1, mov,mp4,m4a,3gp,3g2,mj2, from '1578324600-1-stitched.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.40.101
  
    Duration: 00:05:00.07, start: 0.000000, bitrate: 96 kb/s
  
      Stream #1:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 640x480, 95 kb/s, 10 fps, 25 tbr, 10240 tbn, 20 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
  Input #2, mov,mp4,m4a,3gp,3g2,mj2, from '1578324600-1-stitched.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.40.101
  
    Duration: 00:05:00.07, start: 0.000000, bitrate: 96 kb/s
  
      Stream #2:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 640x480, 95 kb/s, 10 fps, 25 tbr, 10240 tbn, 20 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
  Input #3, mov,mp4,m4a,3gp,3g2,mj2, from '1578324600-1-stitched.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.40.101
  
    Duration: 00:05:00.07, start: 0.000000, bitrate: 96 kb/s
  
      Stream #3:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 640x480, 95 kb/s, 10 fps, 25 tbr, 10240 tbn, 20 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
  [libx264 @ 0x171c9e0] using SAR=1/1
  
  [libx264 @ 0x171c9e0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
  
  [libx264 @ 0x171c9e0] profile High, level 3.1
  
  [libx264 @ 0x171c9e0] 264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=1 ref=2 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=6 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=1 keyint=10 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=10 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
  
  Output #0, mp4, to 'mosaic.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.40.101
  
      Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 1280x720 [SAR 1:1 DAR 16:9], q=-1--1, 10 fps, 10240 tbn, 10 tbc (default)
  
      Metadata:
  
        encoder         : Lavc56.60.100 libx264
  
  Stream mapping:
  
    Stream #0:0 (h264) -> setpts
  
    Stream #1:0 (h264) -> setpts
  
    Stream #2:0 (h264) -> setpts
  
    Stream #3:0 (h264) -> setpts
  
    overlay -> Stream #0:0 (libx264)
  
  Press [q] to stop, [?] for help
  
  [mp4 @ 0x1730600] Starting second pass: moving the moov atom to the beginning of the filerop=4497
  
  frame= 3002 fps= 17 q=-1.0 Lsize=   51052kB time=00:05:00.00 bitrate=1394.1kbits/s dup=0 drop=4498
  
  video:51017kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.068308%
  
  [libx264 @ 0x171c9e0] frame I:301   Avg QP:15.80  size:154257
  
  [libx264 @ 0x171c9e0] frame P:959   Avg QP:21.69  size:  5486
  
  [libx264 @ 0x171c9e0] frame B:1742  Avg QP:22.73  size:   315
  
  [libx264 @ 0x171c9e0] consecutive B-frames: 22.0%  0.0%  5.8% 72.2%
  
  [libx264 @ 0x171c9e0] mb I  I16..4:  9.7% 31.0% 59.4%
  
  [libx264 @ 0x171c9e0] mb P  I16..4:  0.6%  0.7%  0.2%  P16..4: 14.5%  2.8%  2.4%  0.0%  0.0%    skip:78.7%
  
  [libx264 @ 0x171c9e0] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  2.7%  0.3%  0.0%  direct: 1.4%  skip:95.5%  L0:25.9% L1:73.3% BI: 0.9%
  
  [libx264 @ 0x171c9e0] 8x8 transform intra:31.8% inter:43.5%
  
  [libx264 @ 0x171c9e0] coded y,uvDC,uvAC intra: 83.2% 85.2% 69.5% inter: 3.0% 6.3% 0.7%
  
  [libx264 @ 0x171c9e0] i16 v,h,dc,p: 35% 21%  8% 36%
  
  [libx264 @ 0x171c9e0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 27% 42% 10%  3%  2%  3%  6%  4%  5%
  
  [libx264 @ 0x171c9e0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 25% 33%  9%  6%  5%  5%  7%  5%  6%
  
  [libx264 @ 0x171c9e0] i8c dc,h,v,p: 44% 29% 20%  7%
  
  [libx264 @ 0x171c9e0] Weighted P-Frames: Y:0.0% UV:0.0%
  
  [libx264 @ 0x171c9e0] ref P L0: 93.8%  6.2%
  
  [libx264 @ 0x171c9e0] ref B L0: 91.7%  8.3%
  
  [libx264 @ 0x171c9e0] ref B L1: 89.3% 10.7%
  
  [libx264 @ 0x171c9e0] kb/s:1392.16