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• The 11th Hour RoQ Variation

  12 avril 2012, par Multimedia Mike — Game Hacking, dreamroq, Reverse Engineering, roq, Vector Quantization

  I have been looking at the RoQ file format almost as long as I have been doing practical multimedia hacking. However, I have never figured out how the RoQ format works on The 11th Hour, which was the game for which the RoQ format was initially developed. When I procured the game years ago, I remember finding what appeared to be RoQ files and shoving them through the open source decoders but not getting the right images out.

  I decided to dust off that old copy of The 11th Hour and have another go at it.

  
  
  

  Baseline
  The game consists of 4 CD-ROMs. Each disc has a media/ directory that has a series of files bearing the extension .gjd, likely the initials of one Graeme J. Devine. These are resource files which are merely headerless concatenations of other files. Thus, at first glance, one file might appear to be a single RoQ file. So that’s the source of some of the difficulty : Sending an apparent RoQ .gjd file through a RoQ player will often cause the program to complain when it encounters the header of another RoQ file.

  I have uploaded some samples to the usual place.

  However, even the frames that a player can decode (before encountering a file boundary within the resource file) look wrong.

  Investigating Codebooks Using dreamroq
  I wrote dreamroq last year– an independent RoQ playback library targeted towards embedded systems. I aimed it at a gjd file and quickly hit a codebook error.

  RoQ is a vector quantizer video codec that maintains a codebook of 256 2×2 pixel vectors. In the Quake III and later RoQ files, these are transported using a YUV 4:2:0 colorspace– 4 Y samples, a U sample, and a V sample to represent 4 pixels. This totals 6 bytes per vector. A RoQ codebook chunk contains a field that indicates the number of 2×2 vectors as well as the number of 4×4 vectors. The latter vectors are each comprised of 4 2×2 vectors.

  Thus, the total size of a codebook chunk ought to be (# of 2×2 vectors) * 6 + (# of 4×4 vectors) * 4.

  However, this is not the case with The 11th Hour RoQ files.

  Longer Codebooks And Mystery Colorspace
  Juggling the numbers for a few of the codebook chunks, I empirically determined that the 2×2 vectors are represented by 10 bytes instead of 6. Now I need to determine what exactly these 10 bytes represent.

  I should note that I suspect that everything else about these files lines up with successive generations of the format. For example if a file has 640×320 resolution, that amounts to 40×20 macroblocks. dreamroq iterates through 40×20 8×8 blocks and precisely exhausts the VQ bitstream. So that all looks valid. I’m just puzzled on the codebook format.

  Here is an example codebook dump :
  

  ID 0x1002, len = 0x0000014C, args = 0x1C0D
    0 : 00 00 00 00 00 00 00 00 80 80
    1 : 08 07 00 00 1F 5B 00 00 7E 81
    2 : 00 00 15 0F 00 00 40 3B 7F 84
    3 : 00 00 00 00 3A 5F 18 13 7E 84
    4 : 00 00 00 00 3B 63 1B 17 7E 85
    5 : 18 13 00 00 3C 63 00 00 7E 88
    6 : 00 00 00 00 00 00 59 3B 7F 81
    7 : 00 00 56 23 00 00 61 2B 80 80
    8 : 00 00 2F 13 00 00 79 63 81 83
    9 : 00 00 00 00 5E 3F AC 9B 7E 81
    10 : 1B 17 00 00 B6 EF 77 AB 7E 85
    11 : 2E 43 00 00 C1 F7 75 AF 7D 88
    12 : 6A AB 28 5F B6 B3 8C B3 80 8A
    13 : 86 BF 0A 03 D5 FF 3A 5F 7C 8C
    14 : 00 00 9E 6B AB 97 F5 EF 7F 80
    15 : 86 73 C8 CB B6 B7 B7 B7 85 8B
    16 : 31 17 84 6B E7 EF FF FF 7E 81
    17 : 79 AF 3B 5F FC FF E2 FF 7D 87
    18 : DC FF AE EF B3 B3 B8 B3 85 8B
    19 : EF FF F5 FF BA B7 B6 B7 88 8B
    20 : F8 FF F7 FF B3 B7 B7 B7 88 8B
    21 : FB FF FB FF B8 B3 B4 B3 85 88
    22 : F7 FF F7 FF B7 B7 B9 B7 87 8B
    23 : FD FF FE FF B9 B7 BB B7 85 8A
    24 : E4 FF B7 EF FF FF FF FF 7F 83
    25 : FF FF AC EB FF FF FC FF 7F 83
    26 : CC C7 F7 FF FF FF FF FF 7F 81
    27 : FF FF FE FF FF FF FF FF 80 80
  

  Note that 0x14C (the chunk size) = 332, 0x1C and 0x0D (the chunk arguments — count of 2×2 and 4×4 vectors, respectively) are 28 and 13. 28 * 10 + 13 * 4 = 332, so the numbers check out.

  Do you see any patterns in the codebook ? Here are some things I tried :

  • Treating the last 2 bytes as U & V and treating the first 4 as the 4 Y samples :
    
    
    
  • Treating the last 2 bytes as U & V and treating the first 8 as 4 16-bit little-endian Y samples :
    
    
    
  • Disregarding the final 2 bytes and treating the first 8 bytes as 4 RGB565 pixels (both little- and big-endian, respectively, shown here) :
    
    
    
  • Based on the type of data I’m seeing in these movies (which appears to be intended as overlays), I figured that some of these bits might indicate transparency ; here is 15-bit big-endian RGB which disregards the top bit of each pixel :
    
    
    

  These images are taken from the uploaded sample bdpuz.gjd, apparently a component of the puzzle represented in this screenshot.

  Unseen Types
  It has long been rumored that early RoQ files could contain JPEG images. I finally found one such specimen. One of the files bundled early in the uploaded fhpuz.gjd sample contains a JPEG frame. It’s a standard JFIF file and can easily be decoded after separating the bytes from the resource using ‘dd’. JPEGs serve as intraframes in the coding scheme, with successive RoQ frames moving objects on top.

  However, a new chunk type showed up as well, one identified by 0×1030. I have never encountered this type. Where could I possibly find data about this ? Fortunately, iD Games recently posted all of their open sourced games at Github. Reading through the code for their official RoQ decoder, I see that this is called a RoQ_PACKET. The name and the code behind it are both supremely unhelpful. The code is basically a no-op. The payloads of the various RoQ_PACKETs from one sample are observed to be either 8784, 14752, or 14760 bytes in length. It’s very likely that this serves the same purpose as the JPEG intraframes.

  Other Tidbits
  I read through the readme.txt on the first game disc and found this nugget :

          g)      Animations displayed normally or in SPOOKY MODE
                  SPOOKY MODE is blue-tinted grayscale with color cursors, puzzle
  
                  and game pieces.  It is the preferred display setting of the
  
                  developers at Trilobyte.  Just for fun, try out the SPOOKY
  
                  MODE.
  

  The MobyGames screenshot page has a number of screenshots labeled as being captured in spooky mode. Color tricks ?

  Meanwhile, another twist arose as I kept tweaking dreamroq to deal with more RoQ weirdness : After modifying my dreamroq code to handle these 10-byte vectors, it eventually chokes on another codebook. These codebooks happen to have 6-byte vectors again ! Fortunately, I was already working on a scheme to automatically detect which codebook is in play (plugging the numbers into a formula and seeing which vector size checks out).

• What is Google Analytics data sampling and what’s so bad about it ?

  16 août 2019, par Joselyn Khor — Analytics Tips, Development
  What is Google Analytics data sampling, and what’s so bad about it ?

  Google (2019) explains what data sampling is :

  “In data analysis, sampling is the practice of analysing a subset of all data in order to uncover the meaningful information in the larger data set.”[1]

  This is basically saying instead of analysing all of the data, there’s a threshold on how much data is analysed and any data after that will be an assumption based on patterns.

  Google’s (2019) data sampling thresholds :

  Ad-hoc queries of your data are subject to the following general thresholds for sampling :
  [Google] Analytics Standard : 500k sessions at the property level for the date range you are using
  [Google] Analytics 360 : 100M sessions at the view level for the date range you are using (para. 3) [2]

  This threshold is limiting because your data in GA may become more inaccurate as the traffic to your website increases.

  Say you’re looking through all your traffic data from the last year and find you have 5 million page views. Only 500K of that 5 million is accurate ! The data for the remaining 4.5 million (90%) is an assumption based on the 500K sample size.

  This is a key weapon Google uses to sell to large businesses. In order to increase that threshold for more accurate reporting, upgrading to premium Google Analytics 360 for approximately US$150,000 per year seems to be the only choice.

  What’s so bad about data sampling ?

  It’s unfair to say sampled data is to be disregarded completely. There is a calculation ensuring it is representative and can allow you to get good enough insights. However, we don’t encourage it as we don’t just want “good enough” data. We want the actual facts.

  In a recent survey sent to Matomo customers, we found a large proportion of users switched from GA to Matomo due to the data sampling issue.

  The two reasons why data sampling isn’t preferable : 

  1. If the selected sample size is too small, you won’t get a good representative of all the data. 
  2. The bigger your website grows, the more inaccurate your reports will become.

  An example of why we don’t fully trust sampled data is, say you have an ecommerce store and see your GA revenue reports aren’t matching the actual sales data, due to data sampling. In GA you may be seeing revenue for the month as $1 million, instead of actual sales of $800K.

  The sampling here has caused an inaccuracy that could have negative financial implications. What you get in the GA report is an estimated dollar figure rather than the actual sales. Making decisions based on inaccurate data can be costly in this case. 

  Another disadvantage to sampled data is that you might be missing out on opportunities you would’ve noticed if you were given a view of the whole. E.g. not being able to see real patterns occurring due to the data already being predicted. 

  By not getting a chance to see things as they are and only being able to jump to the conclusions and assumptions made by GA is risky. The bigger your business grows, the less you can risk making business decisions based on assumptions that could be inaccurate. 

  If you feel you could be missing out on opportunities because your GA data is sampled data, get 100% accurately reported data. 

  The benefits of 100% accurate data

  Matomo doesn’t use data sampling on any of our products or plans. You get to see all of your data and not a sampled data set.

  Data quality is necessary for high impact decision-making. It’s hard to make strategic changes if you don’t have confidence that your data is reliable and accurate.

  Learn about how Matomo is a serious contender to Google Analytics 360. 

  Now you can import your Google Analytics data directly into your Matomo

  If you’re wanting to make the switch to Matomo but worried about losing all your historic Google Analytics data, you can now import this directly into your Matomo with the Google Analytics Importer tool.

  
  Take the challenge !

  Compare your Google Analytics data (sampled data) against your Matomo data, or if you don’t have Matomo data yet, sign up to our 30-day free trial and start tracking !

  References :

  [1 & 2] About data sampling. (2019). In Analytics Help About data sampling. Retrieved August 14, 2019, from https://support.google.com/analytics/answer/2637192

• A *hot* Piwik Community Meetup 2015 !

  10 août 2015 — Community

  Last weekend I arrived in Germany to attend the Piwik Community Meetup 2015 and now I am in Poland. I joined Piwik PRO back in May as enterprise support project coordinator in North America. I am now writing this from the Piwik PRO main office in Wrocław, where I’ll be working from for the next two weeks.

  The meetup was HOT in every sense ! Berlin temperatures reached 35 degrees (celsius), as I finally meet in person several long-time, dedicated Piwik community contributors.

  

  Meetup preparation in Berlin, photo by M. Zawadziński, licensed under CC-BY-SA 4.0

  Pictures from the meetup preparation sessions
  

  In the first leg of my trip I was in Berlin to meet Piwik community members and Piwik PRO staff to prepare for the 2015 annual Piwik community meetup. These are my notes taken during the meeting at the request of one of my colleagues. I also relayed live on Framasphère, Twitter and IRC.

  

  Community discussion at the meetup, photo by D.Czajka, licensed under CC-BY-SA 4.0

  More pictures from the Piwik meetup
  

  This was harder than I expected, as I took notes with my laptop, pictures with my phone, wrote live to social media (using the Android Diaspora Native Web App), and used my laptop to relay on IRC. Going forward this requires better preparation, I was glad I had a few links and pictures ready before hand but it really requires intense focus to achieve this. I am glad presenters were patient when I requested repeating some of the ideas they shared. I am also a bit disappointed not much happened in IRC.

  Two day preparation sessions

  The discussions and session we had during the two days prior to the meetup are available here.

  We gathered in rented apartments in Berlin, this reminded me very much of similar community gatherings and perhaps of BarCamp and, at a much smaller scale, UDS sessions.

  

  Piwik Pizza !, photo by F. Rodríguez, licensed under CC-BY-SA 4.0

  A list of ideas of topics was initially submitted, we then proceeded to have scheduled sessions for open discussion. Several people shared their concern there was no possible remote participation which led to making public the Trello boards used/linked here.

  Note : The Trello links below still have action items and notes that are pending bug report / feature requests filing which should happen over the coming weeks. Most importantly, many action items will need identifying leads for different community team including Translations and Documentation, and better coordination of coming community engagement.

  Monday sessions consisted of the following subjects :

  • What are Piwik values & how to communicate them ? (see below for details)
  • How to encourage and recognize new external contributors ?
  • How could we double the Piwik userbase ?
  • How Community can organise help resources

  On Tuesday we met again to discuss the following subjects :

  • Piwik Long Term support (LTS)
  • How do Piwik.org (project) and Piwik PRO (company) sit together / are organized ? – An important part of this session was about having better communication channels and improving the new team page (bug #8520 and bug #8519, respectively)
  • Improving usability of Piwik e.g. for new users – this last session was not held has we ran out of time and prepared to go to the meetup venue.

  Some more details about individual preparation sessions

  What are Piwik values & how to communicate them ?

  The main subjects in this session were important changes proposed in the project mission and values. This was edited directly on on the wiki page on GitHub, some of the changes can be seen by comparing revisions.

  Piwik mission statement (bug #7376)

  “To create the leading Free and open source analytics platform, and to support global organisations and communities to keep full control over their data.”

  Our values

  • Openness
  • Freedom
  • Transparency
  • Data ownership
  • Privacy
  • Kaizen (改善) : continuous improvement

  This was also presented by Matthieu Aubry at the meetup and is published in the Roadmap page. Bringing more visibility and perhaps having a top page for Mission and Values was also brought up.

  Meetup agenda and notes

  The official agenda is available here.

  Many Piwik PRO employees stayed in Berlin for the meetup, and we had good participation although less than last year in Munich as my colleagues told me. Some were consultants, others staff from public organizations, universities, etc. In retrospect considering the very hot weather and summer holidays the attendance was good. I was very happy to arrive at the beautiful Kulturbrauerei and enter the air-conditioned Soda Club. T-Shirts were waiting for all attendees and free drinks (non-alcohol !) were welcome