[INCORRIGIBLE REMASTERS] Nomad Megalo Box 2 - Dual Audio - 720p 'Phone Quality' - HEVC

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[INCORRIGIBLE REMASTERS] Nomad Megalo Box 2 - Dual Audio - 720p 'Phone Quality' - HEVC

Category: Anime
Total size: 0.00 kB
Added: 2025-03-10 23:37:31

Share ratio: 5 seeders, 2 leechers
Info Hash: 67ee27523892f7f624074808afbd6beaeeeb4a53
Last updated: 10.8 hours ago

Good evening anime fans. Welcome to the best release of Nomad: Megalo Box 2 the Earth has ever seen. This edition is a 720p release with extra crust, for those who view media on their phones in public transit on bluetooth speakers. Video sourced from JP BDs, subtitles from Foxtrot, audio from JP/US BDs. Using a purpose-trained neural network based on the fabulous RealPLKSR architecture in conjunction with additional denoising and debanding through vapoursynth, I have increased the quality over my last release by a severe measure. Almost all instances of halos and halo-like artifacts are gone, aliasing gone, banding and noise are greatly reduced, and the impact of low-res assets are also almost gone. Here are a few examples of the visual difference between the raw BD and my release. You can look at the full image for these examples and a few more at this link: https://slow.pics/s/odZAUIJL For reference, the main issue with Megalo Box is that ALL releases are upscaled from a 720x405 base, with other post-processing degenerators. It is a digital native anime produced in a higher resolution, and then descaled for “nostalgia”. I disagree with this choice. This is almost as peak as it can get, aside from getting the original higher-res digital cut. The only improvements from here would be with a network that incorporates temporal features rather than being single-image. You can see that there is some degree of shimmering on thin edges that are horizontal to the camera movement, as well as general shimmering in very fine details. This is due to the variable noise in the source material. Unfortunately, the field of temporal video upscaling vs single-image superresolution is much more immature and difficult to implement as a random guy. In either case, I’m much more satisfied with this release. There is only so much information that you can extrapolate from a given degraded image before you get into hallucination territory. Maintaining a balance between image clarity and oversharpening was a challenge while training my model, but I believe that I have achieved the best possible outcome given my constraints in hardware. If you have anything to say about it, the comments are open below, or you can email me at rawhide@neet.works.