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• Error in converting raw yuv video into mp4

  8 décembre 2020, par neetu sigger

  I am trying to convert raw yuv video into mp4 using ffmpeg.

  


  ffmpeg -f rawvideo -vcodec rawvideo -s 1920x1080 -r 25 -pix_fmt yuv420p -i Beauty_3840x2160_120fps_420_10bit_YUV.yuv -c:v libx264 -preset slow -qp 0 bb/output.mp4


  


  I got an error in video.

  


  ffmpeg -f rawvideo -vcodec rawvideo -s 1920x1080 -r 25 -pix_fmt yuv420p -i Beauty_3840x2160_120fps_420_10bit_YUV.yuv -c:v libx264 -preset slow -qp 0 bb/output.mp4
ffmpeg version 4.2.4-1ubuntu0.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.3.0-10ubuntu2)
  configuration: --prefix=/usr --extra-version=1ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
[rawvideo @ 0x7fffe7e28440] Estimating duration from bitrate, this may be inaccurate
Input #0, rawvideo, from 'Beauty_3840x2160_120fps_420_10bit_YUV.yuv':
  Duration: 00:03:12.00, start: 0.000000, bitrate: 622080 kb/s
    Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 1920x1080, 622080 kb/s, 25 tbr, 25 tbn, 25 tbc
File 'bb/output.mp4' already exists. Overwrite ? [y/N] y
Stream mapping:
  Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x7fffe7e35c00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7fffe7e35c00] profile High 4:4:4 Predictive, level 5.0, 4:2:0 8-bit
[libx264 @ 0x7fffe7e35c00] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=5 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=8 psy=0 mixed_ref=1 me_range=16 chroma_me=1 trellis=0 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=0 chroma_qp_offset=0 threads=9 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc=cqp mbtree=0 qp=0
Output #0, mp4, to 'bb/output.mp4':
  Metadata:
    encoder         : Lavf58.29.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 1920x1080, q=-1--1, 25 fps, 12800 tbn, 25 tbc
    Metadata:
      encoder         : Lavc58.54.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
frame= 4800 fps= 16 q=-1.0 Lsize=11907234kB time=00:03:11.96 bitrate=508147.7kbits/s speed=0.646x
video:11907177kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.000485%
[libx264 @ 0x7fffe7e35c00] frame I:2789  Avg QP: 0.00  size:2638070
[libx264 @ 0x7fffe7e35c00] frame P:2011  Avg QP: 0.00  size:2404461
[libx264 @ 0x7fffe7e35c00] mb I  I16..4..PCM: 28.0%  0.0% 67.1%  4.9%
[libx264 @ 0x7fffe7e35c00] mb P  I16..4..PCM:  5.6%  0.0%  6.2%  0.6%  P16..4: 52.9% 23.9% 10.8%  0.0%  0.0%    skip: 0.0%
[libx264 @ 0x7fffe7e35c00] 8x8 transform intra:0.0% inter:9.6%
[libx264 @ 0x7fffe7e35c00] coded y,uvDC,uvAC intra: 99.4% 100.0% 100.0% inter: 100.0% 100.0% 100.0%
[libx264 @ 0x7fffe7e35c00] i16 v,h,dc,p: 89%  0%  1% 10%
[libx264 @ 0x7fffe7e35c00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 65%  0%  0%  0%  0%  0%  0%  0% 35%
[libx264 @ 0x7fffe7e35c00] i8c dc,h,v,p:  1%  0% 94%  4%
[libx264 @ 0x7fffe7e35c00] Weighted P-Frames: Y:31.1% UV:29.3%
[libx264 @ 0x7fffe7e35c00] ref P L0: 21.3% 16.1% 27.4% 14.4% 11.7%  5.5%  3.6%


  


  I am getting the green video.

  


  

  


• Approaches To Modifying Game Resource Files

  16 août 2016, par Multimedia Mike — Game Hacking

  I have been assisting The Translator in the translation of another mid-1990s adventure game. This one isn’t quite as multimedia-heavy as the last title, and the challenges are a bit different. I wanted to compose this post in order to describe my thought process and mental model in approaching this problem. Hopefully, this will help some others understand my approach since what I’m doing here often appears as magic to some of my correspondents.

  High Level Model
  At the highest level, it is valuable to understand the code and the data at play. The code is the game’s engine and the data refers to the collection of resources that comprise the game’s graphics, sound, text, and other assets.

  
  
  Simplistic high-level game engine model
  

  Ideally, we want to change the data in such a way that the original game engine adopts it as its own because it has the same format as the original data. It is very undesirable to have to modify the binary engine executable in any way.

  Modifying The Game Data Directly
  How to modify the data ? If we modify the text strings for the sake of language translation, one approach might be to search for strings within the game data files and change them directly. This model assumes that the text strings are stored in a plain, uncompressed format. Some games might store these strings in a text format which can be easily edited with any text editor. Other games will store them as binary data.
  

  In the latter situation, a game hacker can scan through data files with utilities like Unix ‘strings’ to find the resources with the desired strings. Then, use a hex editor to edit the strings directly. For example, change “Original String”…

  0098F800   00 00 00 00  00 00 00 4F  72 69 67 69  6E 61 6C 20  .......Original 
  0098F810   53 74 72 69  6E 67 00 00  00 00 00 00  00 00 00 00  String..........
  

  …to “Short String” and pad the difference in string lengths using spaces (0x20) :

  0098F800   00 00 00 00  00 00 00 53  68 6F 72 74  20 53 74 72  .......Short Str
  0098F810   69 6E 67 20  20 20 00 00  00 00 00 00  00 00 00 00  ing   ..........
  

  This has some obvious problems. First, translated strings need to be of equal our smaller length compared to the original. What if we want to encode “Much Longer String” ?

  0098F800   00 00 00 00  00 00 00 4D  75 63 68 20  4C 6F 6E 67  .......Much Long
  0098F810   65 72 20 53  74 72 00 00  00 00 00 00  00 00 00 00  er Str..........
  

  It won’t fit. The second problem pertains to character set limitations. If the font in use was only designed for ASCII, it’s going to be inadequate for expressing nearly any other language.

  So a better approach is needed.

  Understanding The Data Structures
  An alternative to the approach outlined above is to understand the game’s resources so they can be modified at a deeper level. Here’s a model to motivate this investigation :

  
  
  Model of the game resource archive format
  

  This is a very common layout for such formats : there is a file header, a sequence of resource blocks, and a trailing index which describes the locations and types of the foregoing blocks.

  What use is understanding the data structures ? In doing so, it becomes possible to write new utilities that disassemble the data into individual pieces, modify the necessary pieces, and then reassemble them into a form that the original game engine likes.

  It’s important to take a careful, experimental approach to this since mistakes can be ruthlessly difficult to debug (unless you relish the thought of debugging the control flow through an opaque DOS executable). Thus, the very first goal in all of this is to create a program that can disassemble and reassemble the resource, thus creating an identical resource file. This diagram illustrates this complex initial process :

  
  
  Rewriting the game resource file
  

  So, yeah, this is one of the most complicated “copy file” operations that I can possibly code. But it forms an important basis, since the next step is to carefully replace one piece at a time.

  
  
  Modifying a specific game resource
  

  This diagram shows a simplistic model of a resource block that contains a series of message strings. The header contains pointers to each of the strings within the block. Instead of copying this particular resource block directly to the new file, a proposed modification utility will intercept it and rewrite the entire thing, writing new strings of arbitrary length and creating an adjusted header which will correctly point to the start of each new string. Thus, translated strings can be longer than the original strings.

  Further Work
  Exploiting this same approach, we can intercept and modify other game resources including fonts, images, and anything else that might need to be translated. I will explore specific examples in a later blog post.

  Followup

  • Translating Return to Ringworld, in which I apply the ideas expressed in this post.

• Meta Receives a Record GDPR Fine from The Irish Data Protection Commission

  29 mai 2023, par Erin — GDPR

  The Irish Data Protection Commission (the DPC) issued a €1.2 billion fine to Meta on May, 22nd 2023 for violating the General Data Protection Regulation (GDPR). 

  The regulator ruled that Meta was unlawfully transferring European users’ data to its US-based servers and taking no sufficient measures for ensuring users’ privacy. 

  Meta must now suspend data transfer within five months and delete EU/EEA users’ personal data that was illegally transferred across the border. Or they risk facing another round of repercussions. 

  Meta continued to transfer personal user data to the USA following an earlier ruling of The Court of Justice of the European Union (CJEU), which already address problematic EU-U.S. data flows. Meta continued those transfers on the basis of the updated Standard Contractual Clauses (“SCCs”), adopted by the European Commission in 2021. 

  The Irish regulator successfully proved that these arrangements had not sufficiently addressed the “fundamental rights and freedoms” of the European data subjects, outlined in the CJEU ruling. Meta was not doing enough to protect EU users’ data against possible surveillance and unconsented usage by US authorities or other authorised entities.

  Why European Regulators Are After The US Big Tech Firms ? 

  GDPR regulations have been a sore area of compliance for US-based big tech companies. 

  Effectively, they had to adopt a host of new measures for collecting user consent, ensuring compliant data storage and the right to request data removal for a substantial part of their user bases. 

  The wrinkle, however, is that companies like Google and Meta among others, don’t have separate data processing infrastructure for different markets. Instead, all the user data gets commingled on the companies’ servers, which are located in the US. 

  Data storage facilities’ location is an issue. In 2020, the CJEU made a historical ruling, called the invalidation of the Privacy Shield. Originally, international companies were allowed to transfer data between the EU and the US if they adhered to seven data protection principles. This arrangement was called the Privacy Shield. 

  However, the continuous investigation found that the Privacy Shield scheme was not GDPR compliant and therefore companies could no longer use it to justify cross-border data transfers.

  The invalidation of the Privacy Shield gave ground for further investigations of the big tech companies’ compliance statuses. 

  In March 2022, the Irish DPC issued the first €17 million fine to Meta for “insufficient technical and organisational measures to ensure information security of European users”. In September 2022, Meta was again hit with a €405 million fine for Instagram breaching GDPR principles. 

  2023 began with another series of rulings, with the DPC concluding that Meta had breaches of the GDPR relating to its Facebook service (€210 million fine) and breaches related to Instagram (€180 million fine). 

  Clearly, Meta already knew they weren’t doing enough for GDPR compliance and yet they refused to take privacy-focused action. 

  Is Google GDPR Compliant ?

  Google has a similar “track record” as Meta when it comes to ensuring full compliance with the GDPR. Although Google has said to provide users with more controls for managing their data privacy, the proposed solutions are just scratching the surface. 

  In the background, Google continues to leverage its ample reserves of user browsing, behavioural and device data in product development and advertising. 

  In 2022, the Irish Council for Civil Liberties (ICCL) found that Google used web users’ information in its real-time bidding ad system without their knowledge or consent. The French data regulator (CNIL), in turn, fined Google for €150 million because of poor cookie consent banners the same year. 

  Google Analytics GDPR compliance status is, however, the bigger concern.

  Neither Google Univeral Analytics (UA) nor Google Analytics 4 are GDPR compliant, following the Privacy Shield framework invalidation in 2020. 

  Fines from individual regulators in Sweden, France, Austria, Italy, Denmark, Finland and Norway ruled that Google Analytics is non-GDPR compliant and is therefore illegal to use. 

  The regulatory rulings not just affect Google, but also GA users. Because the product is in breach of European privacy laws, people using it are complacent. Privacy groups like noyb, for example, are exercising their right to sue individual websites, using Google Analytics.

  How to Stay GDPR Compliant With Website Analytics 

  To avoid any potential risk exposure, selectively investigate each website analytics provider’s data storage and management practices. 

  Inquire about the company’s data storage locations among the first things. For example, Matomo Cloud keeps all the data in the EU, while Matomo On-Premise edition gives you the option to store data in any country of your choice. 

  Secondly, ask about their process for consent tracking and subsequent data analysis. Our website analytics product is fully GDPR compliant as we have first-party cookies enabled by default, offer a convenient option of tracking out-outs, provide a data removal mechanism and practice safe data storage. In fact, Matomo was approved by the French Data Protection Authority (CNIL) as one of the few web analytics apps that can be used to collect data without tracking consent. 

  Using an in-built GDPR Manager, Matomo users can implement the right set of controls for their market and their industry. For example, you can implement extra data or IP anonymization ; disable visitor logs and profiles. 

  Thanks to our privacy-by-design architecture and native controls, users can make their Matomo analytics compliant even with the strictest privacy laws like HIPAA, CCPA, LGPD and PECR. 

  Learn more about GDPR-friendly website analytics.

  Final Thoughts

  Since the GDPR came into effect in 2018, over 1,400 fines have been given to various companies in breach of the regulations. Meta and Google have been initially lax in response to European regulatory demands. But as new fines follow and the consumer pressure mounts, Big Tech companies are forced to take more proactive measures : add opt-outs for personalised ads and introduce an alternative mechanism to third-party cookies. 

  Companies, using non-GDPR-compliant tools risk finding themselves in the crossfire of consumer angst and regulatory criticism. To operate an ethical, compliant business consider privacy-focused alternatives to Google products, especially in the area of website analytics.