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• FFMPEG concat 2 files of different resolution hangs

  6 octobre 2023, par knagode

  I am trying to concat 2 videos of different size and resize it to 426x240 :

  


  ffmpeg -y -i video_1.mp4 -i video_2.mp4 -filter_complex '[0]scale=426:240:force_original_aspect_ratio=decrease,pad=426:240:(ow-iw)/2:(oh-ih)/2,setsar=1[v0];[1]scale=426:240:force_original_aspect_ratio=decrease,pad=426:240:(ow-iw)/2:(oh-ih)/2,setsar=1[v1];[v0][0:a:0][v1][1:a:0]concat=n=2:v=1:a=1[v][a]' -map '[v]' -map '[a]' concatenated_video.mp4


  


  In the output I see :

  


  ffmpeg version 6.0 Copyright (c) 2000-2023 the FFmpeg developers
  built with Apple clang version 14.0.3 (clang-1403.0.22.14.1)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/6.0_1 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libaribb24 --enable-libbluray --enable-libdav1d --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-audiotoolbox
  libavutil      58.  2.100 / 58.  2.100
  libavcodec     60.  3.100 / 60.  3.100
  libavformat    60.  3.100 / 60.  3.100
  libavdevice    60.  1.100 / 60.  1.100
  libavfilter     9.  3.100 /  9.  3.100
  libswscale      7.  1.100 /  7.  1.100
  libswresample   4. 10.100 /  4. 10.100
  libpostproc    57.  1.100 / 57.  1.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'video_1.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf60.3.100
  Duration: 00:00:05.76, start: 0.000000, bitrate: 1582 kb/s
  Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 640x360 [SAR 1:1 DAR 16:9], 1473 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
    Metadata:
      handler_name    : ISO Media file produced by Google Inc. Created on: 08/17/2020.
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.3.100 libx264
  Stream #0:1[0x2](eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 112 kb/s (default)
    Metadata:
      handler_name    : ISO Media file produced by Google Inc. Created on: 08/17/2020.
      vendor_id       : [0][0][0][0]
Input #1, mov,mp4,m4a,3gp,3g2,mj2, from 'video_2.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf60.3.100
  Duration: 00:00:16.40, start: 0.000000, bitrate: 383 kb/s
  Stream #1:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 426x240 [SAR 640:639 DAR 16:9], 245 kb/s, 29.97 fps, 29.97 tbr, 30k tbn (default)
    Metadata:
      handler_name    : Core Media Video
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.3.100 libx264
  Stream #1:1[0x2](eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      handler_name    : Core Media Audio
      vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 (h264) -> scale:default
  Stream #0:1 (aac) -> concat
  Stream #1:0 (h264) -> scale:default
  Stream #1:1 (aac) -> concat
  concat -> Stream #0:0 (libx264)
  concat -> Stream #0:1 (aac)
Press [q] to stop, [?] for help
[vost#0:0/libx264 @ 0x7fc777006280] Frame rate very high for a muxer not efficiently supporting it.
Please consider specifying a lower framerate, a different muxer or setting vsync/fps_mode to vfr
[libx264 @ 0x7fc777006580] using SAR=1/1
[libx264 @ 0x7fc777006580] MB rate (405000000) > level limit (16711680)
[libx264 @ 0x7fc777006580] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7fc777006580] profile High, level 6.2, 4:2:0, 8-bit
[libx264 @ 0x7fc777006580] 264 - core 164 r3095 baee400 - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=7 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'concatenated_video.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf60.3.100
  Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p(tv, progressive), 426x240 [SAR 1:1 DAR 71:40], q=2-31, 1000k tbn
    Metadata:
      encoder         : Lavc60.3.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
  Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s
    Metadata:
      encoder         : Lavc60.3.100 aac
[vost#0:0/libx264 @ 0x7fc777006280] More than 1000 frames duplicated  1.1kbits/s speed=4.94x


  


  Process hangs and I see that ffmpeg uses 500% of the CPU. Any idea how to fix (deal with) this ?

  


  I can open both videos on my computer and play them.

  


• 10 Customer Segments Examples and Their Benefits

  9 mai 2024, par Erin

  Now that companies can segment buyers, the days of mass marketing are behind us. Customer segmentation offers various benefits for marketing, content creation, sales, analytics teams and more. Without customer segmentation, your personalised marketing efforts may fall flat. 

  According to the Twilio 2023 state of personalisation report, 69% of business leaders have increased their investment in personalisation. There’s a key reason for this — customer retention and loyalty directly benefit from personalisation. In fact, 62% of businesses have cited improved customer retention due to personalisation efforts. The numbers don’t lie. 

  Keep reading to learn how customer segments can help you fine-tune your personalised marketing campaigns. This article will give you a better understanding of customer segmentation and real-world customer segment examples. You’ll leave with the knowledge to empower your marketing strategies with effective customer segmentation. 

  What are customer segments ?

  Customer segments are distinct groups of people or organisations with similar characteristics, needs and behaviours. Like different species of plants in a garden, each customer segment has specific needs and care requirements. Customer segments are useful for tailoring personalised marketing campaigns for specific groups.

  Personalised marketing has been shown to have significant benefits — with 56% of consumers saying that a personalised experience would make them become repeat buyers. 

  Successful marketing teams typically focus on these types of customer segmentation :

  

  1. Geographic segmentation : groups buyers based on their physical location — country, city, region or climate — and language.
  2. Purchase history segmentation : categorises buyers based on their purchasing habits — how often they make purchases — and allows brands to distinguish between frequent, occasional and one-time buyers. 
  3. Product-based segmentation : groups buyers according to the products they prefer or end up purchasing. 
  4. Customer lifecycle segmentation : segments buyers based on where they are in the customer journey. Examples include new, repeat and lapsed buyers. This segmentation category is also useful for understanding the behaviour of loyal buyers and those at risk of churning. 
  5. Technographic segmentation : focuses on buyers’ technology preferences, including device type, browser type, and operating system. 
  6. Channel preference segmentation : helps us understand why buyers prefer to purchase via specific channels — whether online channels, physical stores or a combination of both. 
  7. Value-based segmentation : categorises buyers based on their average purchase value and sensitivity to pricing, for example. This type of segmentation can provide insights into the behaviours of price-conscious buyers and those willing to pay premium prices. 

  Customer segmentation vs. market segmentation

  Customer segmentation and market segmentation are related concepts, but they refer to different aspects of the segmentation process in marketing. 

  Market segmentation is the broader process of dividing the overall market into homogeneous groups. Market segmentation helps marketers identify different groups based on their characteristics or needs. These market segments make it easier for businesses to connect with new buyers by offering relevant products or new features. 

  On the other hand, customer segmentation is used to help you dig deep into the behaviour and preferences of your current customer base. Marketers use customer segmentation insights to create buyer personas. Buyer personas are essential for ensuring your personalised marketing efforts are relevant to the target audience. 

  10 customer segments examples

  Now that you better understand different customer segmentation categories, we’ll provide real-world examples of how customer segmentation can be applied. You’ll be able to draw a direct connection between the segmentation category or categories each example falls under.

  One thing to note is that you’ll want to consider privacy and compliance when you are considering collecting and analysing types of data such as gender, age, income level, profession or personal interests. Instead, you can focus on these privacy-friendly, ethical customer segmentation types :

  1. Geographic location (category : geographic segmentation)

  The North Face is an outdoor apparel and equipment company that relies on geographic segmentation to tailor its products toward buyers in specific regions and climates. 

  For instance, they’ll send targeted advertisements for insulated jackets and snow gear to buyers in colder climates. For folks in seasonal climates, The North Face may send personalised ads for snow gear in winter and ads for hiking or swimming gear in summer. 

  The North Face could also use geographic segmentation to determine buyers’ needs based on location. They can use this information to send targeted ads to specific customer segments during peak ski months to maximise profits.

  2. Preferred language (category : geographic segmentation)

  Your marketing approach will likely differ based on where your customers are and the language they speak. So, with that in mind, language may be another crucial variable you can introduce when identifying your target customers. 

  Language-based segmentation becomes even more important when one of your main business objectives is to expand into new markets and target international customers — especially now that global reach is made possible through digital channels. 

  Coca-Cola’s “Share a Coke” is a multi-national campaign with personalised cans and bottles featuring popular names from countries around the globe. It’s just one example of targeting customers based on language.

  3. Repeat users and loyal customers (category : customer lifecycle segmentation)

  Sephora, a large beauty supply company, is well-known for its Beauty Insider loyalty program. 

  It segments customers based on their purchase history and preferences and rewards their loyalty with gifts, discounts, exclusive offers and free samples. And since customers receive personalised product recommendations and other perks, it incentivises them to remain members of the Beauty Insider program — adding a boost to customer loyalty.

  By creating a memorable customer experience for this segment of their customer base, staying on top of beauty trends and listening to feedback, Sephora is able to keep buyers coming back.

  

  4. New customers (category : customer lifecycle segmentation)

  Subscription services use customer lifecycle segmentation to offer special promotions and trials for new customers. 

  HBO Max is a great example of a real company that excels at this strategy : 

  They offer 40% savings on an annual ad-free plan, which targets new customers who may be apprehensive about the added monthly cost of a recurring subscription.

  This marketing strategy prioritises fostering long-term customer relationships with new buyers to avoid high churn rates. 

  5. Cart abandonment (category : purchase history segmentation)

  With a rate of 85% among US-based mobile users, cart abandonment is a huge issue for ecommerce businesses. One way to deal with this is to segment inactive customers and cart abandoners — those who showed interest by adding products to their cart but haven’t converted yet — and send targeted emails to remind them about their abandoned carts.

  E-commerce companies like Ipsy, for example, track users who have added items to their cart but haven’t followed through on the purchase. The company’s messaging often contains incentives — like free shipping or a limited-time discount — to encourage passive users to return to their carts. 

  Research has found that cart abandonment emails with a coupon code have a high 44.37% average open rate. 

  6. Website activity (category : technographic segmentation)

  It’s also possible to segment customers based on website activity. Now, keep in mind that this is a relatively broad approach ; it covers every interaction that may occur while the customer is browsing your website. As such, it leaves room for many different types of segmentation. 

  For instance, you can segment your audience based on the pages they visited, the elements they interacted with — like CTAs and forms — how long they stayed on each page and whether they added products to their cart. 

  Matomo’s Event Tracking can provide additional context to each website visit and tell you more about the specific interactions that occur, making it particularly useful for segmenting customers based on how they spend their time on your website. 

  Try Matomo for Free

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  Amazon segments its customers based on browsing behaviour — recently viewed products and categories, among other things — which, in turn, allows them to improve the customer’s experience and drive sales.

  7. Traffic source (category : channel segmentation) 

  You can also segment your audience based on traffic sources. For example, you can determine if your website visitors arrived through Google and other search engines, email newsletters, social media platforms or referrals. 

  In other words, you’ll create specific audience segments based on the original source. Matomo’s Acquisition feature can provide insights into five different types of traffic sources — search engines, social media, external websites, direct traffic and campaigns — to help you understand how users enter your website.

  You may find that most visitors arrive at your website through social media ads or predominantly discover your brand through search engines. Either way, by learning where they’re coming from, you’ll be able to determine which conversion paths you should prioritise and optimise further. 

  8. Device type (category : technographic segmentation)

  Device type is customer segmentation based on the devices that potential customers may use to access your website and view your content. 

  It’s worth noting that, on a global level, most people (96%) use mobile devices — primarily smartphones — for internet access. So, there’s a high chance that most of your website visitors are coming from mobile devices, too. 

  However, it’s best not to assume anything. Matomo can detect the operating system and the type of device — desktop, mobile device, tablet, console or TV, for example. 

  By introducing the device type variable into your customer segmentation efforts, you’ll be able to determine if there’s a preference for mobile or desktop devices. In return, you’ll have a better idea of how to optimise your website — and whether you should consider developing an app to meet the needs of mobile users.

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  9. Browser type (category : technographic segmentation)

  Besides devices, another type of segmentation that belongs to the technographic category and can provide valuable insights is browser-related. In this case, you’re tracking the internet browser your customers use. 

  Many browser types are available — including Google Chrome, Microsoft Edge, Safari, Firefox and Brave — and each may display your website and other content differently. 

  So, keeping track of your customers’ preferred choices is important. Otherwise, you won’t be able to fully understand their online experience — or ensure that these browsers are displaying your content properly. 

  

  10. Ecommerce activity (category : purchase history, value based, channel or product based segmentation) 

  Similar to website activity, looking at ecommerce activity can tell your sales teams more about which pages the customer has seen and how they have interacted with them. 

  With Matomo’s Ecommerce Tracking, you’ll be able to keep an eye on customers’ on-site behaviours, conversion rates, cart abandonment, purchased products and transaction data — including total revenue and average order value.

  Considering that the focus is on sales channels — such as your online store — this approach to customer segmentation can help you improve the sales experience and increase profitability. 

  Start implementing these customer segments examples

  With ever-evolving demographics and rapid technological advancements, customer segmentation is increasingly complex. The tips and real-world examples in this article break down and simplify customer segmentation so that you can adapt to your customer base. 

  Customer segmentation lays the groundwork for your personalised marketing campaigns to take off. By understanding your users better, you can effectively tailor each campaign to different segments. 

  If you’re ready to see how Matomo can elevate your personalised marketing campaigns, try it for free for 21 days. No credit card required.

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• Why does ffmpeg keep using more and more RAM and crash ?

  29 décembre 2022, par József Márton Kakas

  I am using the following command with ffmpeg to encode a video file using the libsvtav1 codec : ffmpeg -i hevc.mkv -map 0:v:0 -c:v:0 libsvtav1 -preset 8 -crf 22 -format matroska av1.mkv.

  


  However, ffmpeg is using up all of my available RAM and crashing. I have also tried using the libx264 codec, but the same issue occurs, although it happens more slowly. I have already allocated 12 GB of RAM to ffmpeg, but it still seems to be insufficient. How can I prevent ffmpeg from using all of my available RAM and crashing when using either the libsvtav1 or libx264 codecs ?

  


  I have tried it on another VM, but the same issue occurs. Here is the full output of the program.

  


  ffmpeg -i akira.mkv -map 0:v:0 -c:v:0 libsvtav1 -preset 8 -crf 22 -c:a copy -c:s copy -format matroska av1.mkv
ffmpeg version n5.1.2-7-ga6e26053c2-20221106 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12.2.0 (crosstool-NG 1.25.0.90_cf9beb1)
  configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-ffbuild-linux-gnu- --arch=x86_64 --target-os=linux --enable-gpl --enable-version3 --disable-debug --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --enable-libpulse --enable-libvmaf --enable-libxcb --enable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --disable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-mbedtls --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libmfx --disable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --disable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --enable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags=-pie --extra-libs='-ldl -lgomp' --extra-version=20221106
  libavutil      57. 28.100 / 57. 28.100
  libavcodec     59. 37.100 / 59. 37.100
  libavformat    59. 27.100 / 59. 27.100
  libavdevice    59.  7.100 / 59.  7.100
  libavfilter     8. 44.100 /  8. 44.100
  libswscale      6.  7.100 /  6.  7.100
  libswresample   4.  7.100 /  4.  7.100
  libpostproc    56.  6.100 / 56.  6.100
Input #0, matroska,webm, from 'akira.mkv':
  Metadata:
    title           : Akira 4K
    encoder         : libebml v1.3.10 + libmatroska v1.5.2
    creation_time   : 2020-05-04T19:57:18.000000Z
  Duration: 02:04:46.50, start: 0.000000, bitrate: 10945 kb/s
  Stream #0:0: Video: hevc (Main 10), yuv420p10le(tv, bt2020nc/bt2020/smpte2084), 3840x2074 [SAR 1:1 DAR 1920:1037], 23.98 fps, 23.98 tbr, 1k tbn (default)
    Metadata:
      BPS-eng         : 9531297
      DURATION-eng    : 02:04:46.479000000
      NUMBER_OF_FRAMES-eng: 179496
      NUMBER_OF_BYTES-eng: 8919482644
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:1(eng): Subtitle: subrip
    Metadata:
      BPS-eng         : 46
      DURATION-eng    : 01:59:00.755000000
      NUMBER_OF_FRAMES-eng: 1277
      NUMBER_OF_BYTES-eng: 41675
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:2(ita): Audio: ac3, 48000 Hz, 5.1(side), fltp, 448 kb/s (default)
    Metadata:
      title           : Nuovo doppiaggio
      BPS-eng         : 448000
      DURATION-eng    : 02:04:28.480000000
      NUMBER_OF_FRAMES-eng: 233390
      NUMBER_OF_BYTES-eng: 418234880
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:3(ita): Audio: ac3, 48000 Hz, stereo, fltp, 448 kb/s
    Metadata:
      title           : Doppiaggio Storico
      BPS-eng         : 448000
      DURATION-eng    : 02:04:28.480000000
      NUMBER_OF_FRAMES-eng: 233390
      NUMBER_OF_BYTES-eng: 418234880
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:4(ita): Subtitle: dvd_subtitle, 1920x1080
    Metadata:
      title           : Italiano
      BPS-eng         : 8426
      DURATION-eng    : 02:00:14.955000000
      NUMBER_OF_FRAMES-eng: 1201
      NUMBER_OF_BYTES-eng: 7599491
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:5(ita): Subtitle: subrip (default)
    Metadata:
      title           : Forced
      BPS-eng         : 0
      DURATION-eng    : 01:46:31.753000000
      NUMBER_OF_FRAMES-eng: 14
      NUMBER_OF_BYTES-eng: 462
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:6(jpn): Audio: eac3, 48000 Hz, 5.1(side), fltp, 256 kb/s
    Metadata:
      BPS-eng         : 256000
      DURATION-eng    : 02:04:46.496000000
      NUMBER_OF_FRAMES-eng: 233953
      NUMBER_OF_BYTES-eng: 239567872
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:7(eng): Audio: eac3, 48000 Hz, 5.1(side), fltp, 256 kb/s
    Metadata:
      BPS-eng         : 256000
      DURATION-eng    : 02:04:46.496000000
      NUMBER_OF_FRAMES-eng: 233953
      NUMBER_OF_BYTES-eng: 239567872
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:8: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 1067x600, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : cover_land.jpg
      mimetype        : image/jpeg
  Stream #0:9: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 120x176, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : small_cover.jpg
      mimetype        : image/jpeg
  Stream #0:10: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 213x120, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : small_cover_land.jpg
      mimetype        : image/jpeg
  Stream #0:11: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 600x882, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : cover.jpg
      mimetype        : image/jpeg
  Stream #0:12: Attachment: otf
    Metadata:
      filename        : StoneSansStd-Semibold.otf
      mimetype        : application/vnd.ms-opentype
  Stream #0:13: Attachment: otf
    Metadata:
      filename        : StoneSansStd-SemiboldItalic.otf
      mimetype        : application/vnd.ms-opentype
  Stream #0:14: Attachment: otf
    Metadata:
      filename        : ATPacella-Black.otf
      mimetype        : application/vnd.ms-opentype
  Stream #0:15: Attachment: ttf
    Metadata:
      filename        : ITC Franklin Gothic LT Medium Condensed.ttf
      mimetype        : application/x-truetype-font
File 'av1.mkv' already exists. Overwrite? [y/N] y
Stream mapping:
  Stream #0:0 -> #0:0 (hevc (native) -> av1 (libsvtav1))
Press [q] to stop, [?] for help
Svt[info]: -------------------------------------------
Svt[info]: SVT [version]:       SVT-AV1 Encoder Lib v1.3.0
Svt[info]: SVT [build]  :       GCC 12.2.0       64 bit
Svt[info]: LIB Build date: Nov  6 2022 00:41:54
Svt[info]: -------------------------------------------
Svt[info]: Number of logical cores available: 16
Svt[info]: Number of PPCS 71
Svt[info]: [asm level on system : up to avx]
Svt[info]: [asm level selected : up to avx]
Svt[info]: -------------------------------------------
Svt[info]: SVT [config]: main profile   tier (auto)     level (auto)
Svt[info]: SVT [config]: width / height / fps numerator / fps denominator               : 3840 / 2080 / 24000 / 1001
Svt[info]: SVT [config]: bit-depth / color format                      : 10 / YUV420
Svt[info]: SVT [config]: preset / tune / pred struct                   : 8 / PSNR / random access
Svt[info]: SVT [config]: gop size / mini-gop size / key-frame type     : 161 / 16 / key frame
Svt[info]: SVT [config]: BRC mode / rate factor                        : CRF / 22 
Svt[info]: -------------------------------------------
Output #0, matroska, to 'av1.mkv':
  Metadata:
    title           : Akira 4K
    encoder         : Lavf59.27.100
  Stream #0:0: Video: av1 (AV01 / 0x31305641), yuv420p10le(tv, bt2020nc/bt2020/smpte2084, progressive), 3840x2074 [SAR 1:1 DAR 1920:1037], q=2-31, 23.98 fps, 1k tbn (default)
    Metadata:
      BPS-eng         : 9531297
      DURATION-eng    : 02:04:46.479000000
      NUMBER_OF_FRAMES-eng: 179496
      NUMBER_OF_BYTES-eng: 8919482644
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
      encoder         : Lavc59.37.100 libsvtav1
frame=    1 fps=0.0 q=0.0 size=       1kB time=00:00:00.00 bitrate=N/A sframe=    2 fps=1.3 q=0.0 size=       1kB time=00:00:00.00 bitrate=N/A sframe=   51 fps= 25 q=0.0 size=       1kB time=00:00:00.00 bitrate=N/A sframe=   73 fps= 16 q=16.0 size=       1kB time=00:00:00.00 bitrate=7480frame=   76 fps= 14 q=16.0 size=       1kB time=00:00:00.00 bitrate=7480frame=   81 fps= 14 q=16.0 size=       1kB time=00:00:00.00 bitrate=7480frame=   87 fps= 14 q=21.0 size=       1kB time=00:00:00.16 bitrate=  44frame=   95 fps= 14 q=21.0 size=       1kB time=00:00:00.50 bitrate=  14frame=  100 fps= 13 q=16.0 size=       1kB time=00:00:00.66 bitrate=  11frame=  110 fps= 13 q=23.0 size=       1kB time=00:00:00.96 bitrate=   7frame=  114 fps= 13 q=23.0 size=       1kB time=00:00:01.12 bitrate=   6frame=  122 fps= 13 q=23.0 size=       1kB time=00:00:01.46 bitrate=   5frame=  126 fps= 12 q=23.0 size=       1kB time=00:00:01.62 bitrate=   4frame=  133 fps= 12 q=22.0 size=       1kB time=00:00:01.92 bitrate=   3frame=  139 fps= 12 q=21.0 size=       1kB time=00:00:02.17 bitrate=   3frame=  141 fps= 11 q=22.0 size=       1kB time=00:00:02.25 bitrate=   3frame=  144 fps= 11 q=23.0 size=       1kB time=00:00:02.37 bitrate=   3frame=  151 fps= 11 q=13.0 size=       1kB time=00:00:02.67 bitrate=   2frame=  159 fps= 11 q=18.0 size=       1kB time=00:00:03.00 bitrate=   2frame=  164 fps= 11 q=22.0 size=       1kB time=00:00:03.21 bitrate=   2frame=  174 fps= 11 q=22.0 size=       1kB time=00:00:03.63 bitrate=   2frame=  179 fps= 11 q=20.0 size=       1kB time=00:00:03.83 bitrate=   1frame=  187 fps= 11 q=20.0 size=       1kB time=00:00:04.17 bitrate=   1frame=  189 fps= 10 q=21.0 size=       1kB time=00:00:04.25 bitrate=   1frame=  192 fps= 10 q=22.0 size=       1kB time=00:00:04.38 bitrate=   1frame=  195 fps=8.0 q=20.0 size=       1kB time=00:00:04.50 bitrate=   1frame=  201 fps=8.0 q=11.0 size=       1kB time=00:00:04.67 bitrate=   1Killeds/s speed=0.187x