![]() The marketing speil on their website clearly indicates that the primary use case is to upscale 1080p footage to 4k and beyond, so I was almost certainly working outside the training data. Each of these have then been trained on differing qualities of video (referred to as LQ, MQ and HQ, though exact resolutions etc were not provided). The Topaz AI Enhance product has a 30 day trial, and currently offers 2 Upscaling AI models.Gaia and Artemis. My knowledge of AI processes is limited, based on a Udacity Intro to Tensorflow.which taught me enough to understand that half the challenge of building a Machine-Learning AI is structuring it, the other half is providing suitable training data (and with my work hat on, I think most people have no idea that the training data is so important). I had a suitable machine sitting round (a rather middle-ground i3 processor with a GTX1660, with the required CUDA cores). There have been some examples of it being used to restore old footage, and in combination with DAIN (something co-developed with Google) to produce 4K60 footage of very old video.however looking at the source material those projects used, it was cleaner than the 240p VHS rips that I had.nevertheless, I thought I'd give it a go. I used one of the myriad Youtube downloaders to pull out video files of them.I could just chuck them on the server as-is, however I wondered with the recent advances in AI Upscaling, was there any way of improving the footage?Ī small amount of research leg me to Topaz AI Labs, and specifically their AI Enhance video product. The In Pieces footage was 360p and slightly better. The Mary Whitehouse Experience footage was 240p, and fairly badly compressed. ![]() Neither have been repeated, released on DVD etc, and in all probability never will be. One of the rareties that I found on Youtube was someone had uploaded some really low quality VHS rips of The Mary Whitehouse Experience and Newman and Baddiel in Pieces. A part of my mini-project to fill up my in-house media server, I've been hunting down some older stuff I liked. ![]()
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