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• I have an MJPEG file decoded with the Intel® IPP JPEG encoder [8.1.42291], how can I convert it to MP4 using ffmpeg ? [closed]

  8 mars 2024, par Renan Aragão

  When trying to convert the MJPEG file with ffmpeg, it gives the following error : Output file #0 does not contain any stream.

  


  This is the complete output :

  


  .\ffmpeg.exe -init_hw_device qsv=intel,child_device=1 -filter_hw_device intel -i .\19a57e41-244c-45a4-af41-5069dd3cbe4a.tmp -an -vf "format=nv12,hwupload=extra_hw_frames=20" -c:v h264_qsv record.mp4 -y -loglevel verbose
ffmpeg version n5.0.1-5-g240d82f26e-20220613 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 11.2.0 (crosstool-NG 1.24.0.533_681aaef)
  configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --disable-avisynth --enable-libdav1d --disable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --disable-frei0r --enable-libgme --enable-libass --enable-libbluray --enable-libmp3lame --enable-libopus --enable-librist --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libmfx --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --disable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --disable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --disable-libx264 --disable-libx265 --disable-libxavs2 --disable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20220613
  libavutil      57. 17.100 / 57. 17.100
  libavcodec     59. 18.100 / 59. 18.100
  libavformat    59. 16.100 / 59. 16.100
  libavdevice    59.  4.100 / 59.  4.100
  libavfilter     8. 24.100 /  8. 24.100
  libswscale      6.  4.100 /  6.  4.100
  libswresample   4.  3.100 /  4.  3.100
WARNING: defaulting child_device_type to AV_HWDEVICE_TYPE_DXVA2 for compatibility with old commandlines. This behaviour will be removed in the future. Please explicitly set device type via "-init_hw_device" option.
[AVHWDeviceContext @ 000001953dac2f40] Using D3D9Ex device.
[AVHWDeviceContext @ 000001953dac2a80] Initialize MFX session: API version is 1.35, implementation version is 1.35
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '.\19a57e41-244c-45a4-af41-5069dd3cbe4a.tmp':
  Metadata:
    major_brand     : mp42
    minor_version   : 0
    compatible_brands: mp42isom
    creation_time   : 2024-03-06T13:32:38.000000Z
  Duration: 00:00:00.00, bitrate: N/A
Output #0, mp4, to 'record.mp4':
Output file #0 does not contain any stream


  


  I try using ipp in python and dotnet, but idon't found sample code. I need convert MJPEG Intel IPP to mp4.

  


• Increased File Size When Converting MP4 to WebM using FFmpeg

  23 décembre 2024, par kimgijeong

  I am using FFmpeg to convert MP4 to WebM with the following command :

  


  ffmpeg -y -hide_banner -nostats \
-f mov,mp4,m4a,3gp,3g2,mj2 -i "http://127.0.0.1:80/lotteon-low-bitrate.mp4" \
-threads auto -f webm -acodec libopus -b:a 96.059k -vcodec libsvtav1 -preset 11 -pix_fmt yuv420p \
-vf "scale='min(-1, iw)':'min(-1,ih)':force_original_aspect_ratio=decrease,crop=trunc(iw/2)*2:trunc(ih/2)*2" \
"/usr/local/m2/m2temp/xcdrtmp/2052_1.webm"


  


  However, the output webm file size is larger than the source MP4 file. For example :

  


  


  • Source MP4 : 4.6 MB (bit rate : 994,053 bps)

    


  


  • Output WebM : 16 MB (bit rate : 3,902,037 bps)

    


  


  


  I know SVT-AV1 encoder defaults to CRF mode. Due to not specifying the bitrate explicitly, the SVT-AV1 encoder automatically sets the bit_rate. It appears that the encoder is setting it to a much higher value (3,323,104 bps), causing the increase in file size compared to the source MP4 (994,053 bps). Here are the methods i tried to reduce the WebM file size compared to the source MP4 :

  


  

  1. -b:v 994k

  


  


  I tried to match the target bitrate with the source MP4's bitrate to reduce the output size, but encountered the following error :

  


  Svt[error]: Instance 1: Force key frames is not supported for VBR mode Last message r
epeated 2 times [libsvtav1 @ 0x239dd100] Error setting encoder parameters: bad parameter (0x80001005)


  


  Looking at the official documentation, this mode change (from CRF to VBR when setting a target bitrate) appears to be expected behavior. However, the error is puzzling since I haven't set any force keyframe parameters in the FFmpeg command.

  


  

  2. svtav1-params "mbr=994k"

  


  


  The second method i tried was using the svtav1-params "mbr=994k" option to set the maxrate while maintaining CRF mode This method showed some improvement, but the output file size was still larger than the source MP4.

  


  

  • Output WebM : 5MB (bit rate : 1,209,877 bps)

  


  


  The more critical reason why we can't adopt the second method (using svtav1-params "mbr=994k") is that even for the same MP4 source file, the output file size varies slightly with each encoding.

  


  

  3. -b:v 994k -svtav1-params “rc=2:pred-struct=1”(CBR, low delay)

  


  


  The final method I tried was setting the target bitrate while using CBR (Constant Bit Rate) and low-delay mode The default value for pred-structure is 2(random access), but I had to use low-delay mode due to the following error :

  


  Svt[error]: CBR Rate control is currently not supported for SVT_AV1_PRED_RANDOM_ACCESS, use VBR mode


  


  This way was the only approach among those i tried that successfully reduced the output size.

  


  

  • Output WebM : 4.3MB (bit rate : 1,032,373 bps)

  


  


  In summary, I have three questions about this MP4 to WebM conversion issue :

  


  


  1. When setting the target bitrate with -b:v 994k, the switch to VBR mode is expected behavior. However, why does the force keyframe error occur when we haven't explicitly set any force keyframe parameters ?

     


  


  2. Why does the output WebM file size fluctuate when setting maxrate either through -maxrate or svtav1-params "mbr=994k", even when using the same MP4 source file ?

     


  


  3. Besides using -b:v 994k -svtav1-params "rc=2:pred-struct=1" (CBR with low delay), are there any other methods that can guarantee a WebM output size smaller than the source MP4 when converting from MP4 to WebM ?

     


  


  


  I am using a recent version of the SVT-AV1 codec :

  


  Svt[info]: SVT [version]:       SVT-AV1 Encoder Lib 58146ca
Svt[info]: SVT [build]  :       GCC 11.5.0 20240719 (Red Hat 11.5.0-2)   64 bit
Svt[info]: LIB Build date: Oct 28 2024 07:40:59
ffmpeg video svt-av1


  


• What is Web Log Analytics and Why You Should Use It

  26 juin 2024, par Erin

  Can’t use JavaScript tracking on your website ? Need a more secure and privacy-friendly way to understand your website visitors ? Web log analytics is your answer. This method pulls data directly from your server logs, offering a secure and privacy-respecting alternative.  

  In this blog, we cover what web log analytics is, how it compares to JavaScript tracking, who it is best suited for, and why it might be the right choice for you. 

  What are server logs ? 

  Before diving in, let’s start with the basics : What are server logs ? Think of your web server as a diary that notes every visit to your website. Each time someone visits, the server records details like : 

  • User agent : Information about the visitor’s browser and operating system. 

  • Timestamp : The exact time the request was made. 

  • Requested URL : The specific page or resource the visitor requested. 

  These “diary entries” are called server logs, and they provide a detailed record of all interactions with your website. 

  Server log example 

  Here’s what a server log looks like : 

  192.XXX.X.X – – [24/Jun/2024:14:32:01 +0000] “GET /index.html HTTP/1.1” 200 1024 “https://www.example.com/referrer.html” “Mozilla/5.0 (Windows NT 10.0 ; Win64 ; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36” 

  192.XXX.X.X – – [24/Jun/2024:14:32:02 +0000] “GET /style.css HTTP/1.1” 200 3456 “https://www.example.com/index.html” “Mozilla/5.0 (Windows NT 10.0 ; Win64 ; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36” 

  192.XXX.X.X – – [24/Jun/2024:14:32:03 +0000] “GET /script.js HTTP/1.1” 200 7890 “https://www.example.com/index.html” “Mozilla/5.0 (Windows NT 10.0 ; Win64 ; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36” 

  192.XXX.X.X – – [24/Jun/2024:14:32:04 +0000] “GET /images/logo.png HTTP/1.1” 200 1234 “https://www.example.com/index.html” “Mozilla/5.0 (Windows NT 10.0 ; Win64 ; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36” 

  Breakdown of the log entry 

  Each line in the server log represents a single request made by a visitor to your website. Here’s a detailed breakdown of what each part means : 

  • IP Address : 192.XXX.X.X 
    • This is the IP address of the visitor’s device. 
  • User Identifier : – – 
    • These fields are typically used for user identification and authentication, which are not applicable here, hence the hyphens. 
  • Timestamp : [24/Jun/2024:14:32:01 +0000] 
  • • • The date and time of the request, including the timezone. 
  • Request Line : “GET /index.html HTTP/1.1” 
    • The request method (GET), the requested resource (/index.html), and the HTTP version (HTTP/1.1). 
  • Response Code : 200 
    • The HTTP status code indicates the result of the request (200 means OK). 
  • Response Size : 1024 
    • The size of the response in bytes. 
  • Referrer : “https://www.example.com/referrer.html“ 
    • The URL of the referring page that led the visitor to the current page. 
  • User Agent : “Mozilla/5.0 (Windows NT 10.0 ; Win64 ; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36” 
    • Information about the visitor’s browser and operating system. 

  In the example above, there are multiple log entries for different resources (HTML page, CSS file, JavaScript file, and an image). This shows that when a visitor loads a webpage, multiple requests are made to load all the necessary resources. 

  What is web log analytics ? 

  Web log analytics is one of many methods for tracking visitors to your site.  

  

  Web log analytics is the process of analysing server log files to track and understand website visitors. Unlike traditional methods that use JavaScript tracking codes embedded in web pages, web log analytics pulls data directly from these server logs. 

  How it works : 

  1. Visitor request : A visitor’s browser requests your website. 
  2. Server logging : The server logs the request details. 
  3. Analysis : These logs are analysed to extract useful information about your visitors and their activities. 

  Web log analytics vs. JavaScript tracking 

  JavaScript tracking 

  JavaScript tracking is the most common method used to track website visitors. It involves embedding a JavaScript code snippet into your web pages. This code collects data on visitor interactions and sends it to a web analytics platform. 

  

  Differences and benefits :
  

  Privacy : 

  • Web log analytics : Since it doesn’t require embedding tracking codes, it is considered less intrusive and helps maintain higher privacy standards. 

  • JavaScript tracking : Embeds tracking codes directly on your website, which can be more invasive and raise privacy concerns. 

  Ease of setup : 

  • Web log analytics : No need to modify your website’s code. All you need is access to your server logs. 

  • JavaScript tracking : Requires adding tracking code on your web pages. This is generally an easier setup process.  

  Data collection : 

  • Web log analytics : Contain requests of users with adblockers (ghostery, adblock, adblock plus, privacy badger, etc.) sometimes making it more accurate. However, it may miss certain interactive elements like screen resolution or user events. It may also over-report data.  

  • JavaScript tracking : Can collect a wide range of data, including Custom dimensions, Ecommerce tracking, Heatmaps, Session recordings, Media and Form analytics, etc. 

  Why choose web log analytics ? 

  Enhanced privacy 

  Avoiding embedded tracking codes means there’s no JavaScript running on your visitors’ browsers. This significantly reduces the risk of data leakage and enhances overall privacy. 

  Comprehensive data collection 

  It isn’t affected by ad blockers or browser tracking protections, ensuring you capture more complete and accurate data about your visitors. 

  Historical data analysis 

  You can import and analyse historical log files, giving you insights into long-term visitor behaviour and trends. 

  Simple setup 

  Since it relies on server logs, there’s no need to alter your website’s code. This makes setup straightforward and minimises potential technical issues. 

  Who should use web log analytics ? 

  Web log analytics is particularly suited for businesses that prioritise data privacy and security.

  Organisations that handle sensitive data, such as banks, healthcare providers, and government agencies, can benefit from the enhanced privacy.  

  By avoiding JavaScript tracking, these entities minimise data exposure and comply with strict privacy regulations like Sarbanes Oxley and PCI. 

  Why use Matomo for web log analytics ? 

  Matomo stands out as a top choice for web log analytics because it prioritises privacy and data ownership

  

  Here’s why : 

  • Complete data control : You own all your data, so you don’t have to worry about third-party access. 
  • IP anonymisation : Matomo anonymises IP addresses to further protect user privacy. 
  • Bot filtering : Automatically excludes bots from your reports, ensuring you get accurate data. 
  • Simple migration : You can easily switch from other tools like AWStats by importing your historical logs into Matomo. 
  • Server log recognition : Recognises most server log formats (Apache, Nginx, IIS, etc.). 

  Start using web log analytics 

  Web log analytics offers a secure, privacy-focused alternative to traditional JavaScript tracking methods. By analysing server logs, you get valuable insights into your website traffic while maintaining high privacy standards.  

  If you’re serious about privacy and want reliable data, give Matomo’s web log analytics a try.  

  Start your 21-day free trial now. No credit card required. 

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