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Game Preservation
gbatemp.net
This is a really neat way to stream your 3DS games to your computer, to play on livestreams for instance
archive.org
[Long story short](https://youtu.be/7Fk6yDKhFR8): during the lead up to the English release of *Pokémon the Movie 2000*, Nintendo gave Warner Bros. permission to release tie-in products to promote it. Warner Bros. opted to commission Cyberworld International Corporation to create a short browser game to that end. According to Eddie Ruminski, an employee who kept the files and allowed them to be shared online, the game was downloaded more than 1,000,000 times within a month - which led to Nintendo forcibly shutting it down, as they felt the game was "[exceeding their] contract's allowable scope."
youtu.be
Using a VGA to DoCoMo flat-plug adapter, I've recorded GUNDAM U.C.0079, a preloaded game on the P906i feature phone. This game is a 3D third person shooter that runs impressively for a 2008 flip phone game.
Recently I found a cell phone from Japan. The model of this phone is W52SH. This phone contained a lost BREW game Mobile Pro Power Baseball 5. To purchase this game, you would need to have a AU BREW cell phone, and access to the EZ appli store while this game was in service. The store for EZ appli shut down in 2018. Which means even now if the right phone and 3G, paying for the game is not possible. But we found a W52SH that did contain the game. We decided to preserve this lost media due to the fact that obtaining the game as of now is no longer possible. If you have anymore questions about iMode, AU and Softbank video game preservation, you are more than welcome to ask. https://archive.org/details/powapuro5
archive.org
A while back, I discovered that one-hit-wonder euro band Rednex actually had a game based on their follow-up album. It didn't appear to do too well, but someone was nice enough to archive the game. Since I don't have the time or resources to hunt down the original recordings like the wonderful efforts of [Conversations with Curtis](https://www.youtube.com/watch?v=D0ZKvPQ4uH8&list=PL9qFy7xdUfuQTLm1wjsmEz-SggxnKDoek&index=1&pp=iAQB), I figured the least I could do was extract the FMV files and preserve them in a modern readable format. No attempts to upscale, so enjoy in their original small crunchy goodness completely out of game context. 'Konut' is a personal favorite
A few months ago I was doings some reading on the theory and algorithms for decreening scans (also called inverse-halftoning) to try and understand how tools like Sattva Descreen work and what other alternate algorithmic approach exist (to try and understand what different tradeoffs are possible). I might make a discussion post later about some of my thoughts and questions about the possible tradeoffs (mostly related to retaining line clarity), but I figured for now I should share some of the resources I found. (All of the papers mentioned below are available online as PDFs, a search of the title and first author should find them) # Best introduction paper "Inverse Halftoning Using Inverse Methods" (Gustavsson, 2007) gives a really nice introduction to the theory behind halftoning and discusses several of the different approaches for descreening algorithms that were in the literature at the time. I highly recommend reading through at least Chapters 2 & 3. They are very approachable and informative. Also the author gets brownie points from me for criticizing some papers on algorithmic approaches for not testing with actually scanned images. # Other papers - Inverse Halftoning Using Wavelets (Xiong wt al., 1997) - This paper is almost entirely math that is well outside of my knowledge, but it looks like it does something with using edge information to improve descreening. - Recent Advances in Digital Halftoning and Inverse Halftoning Methods (Me ̧se & Vaidyanathan, 2001) - This paper discusses a Look Up Table-based method for descreening (different from the typical fourier transform + Gaussian blur approach). Though I don't know how this compares to other algorithmic approaches for descreening. (another very math heavy paper) - Deep Joint Image Filtering (Li et al., 2016) - This paper discusses an interesting Convolutional Neural Newtork-based approach for descreening (and some other related processes) and is a *relatively* recent paper. I don't understand the math behind it, but the idea of using deep learning to pick up on the relationships between non-screened and screened versions of images sounds promising. I imagine one of the big challenges with approaches like this is getting a good training set to work with, especially making sure to have a training set comprised mostly or at least containing a lot of real scanned images (as opposed to just applying digital halftoning to images and using those digital halftones for training).