Splash – Fractal zooming with a paint brush as video encoding

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Splash [0] is novel method to serialize pixels of an image/video. It combines row/column scanning weights from the fractal zoomer [1] with an adaptive paintbrush for colouring pixels with transparency. In traditional terms, fast changing areas have a localised high FPS, lesser changing areas have a localised low FPS. This could, for example, greatly benefit the communication between video card and monitor, relaxing bandwidth limitations. Splash can also be used as a filter to isolate essence of movement, demonstrated by extreme high PixelsPerFrame ratios with other demos [2]. Splash could effectively turn a 60Hz into a 360Hz responsive monitor by sacrificing a bit of quality. This is illustrated by the demo below [3], original video is 24fps and slowed down 6x: top: 4fps full frame with traditional scan-lines, bottom: 1/6 PixelsPerFrame with scan-rows/cols and paint-brush. Yes... both are using the same number of pixels (uncompressed bandwidth). [0] https://rockingship.github.io/splash/README.html [1] https://rockingship.github.io/jsFractalZoom/README.html [2] https://rockingship.github.io/splash-media [3] https://rockingship.github.io/splash-media/starships/starships-1212x510-OU-crf18.mp4

Visit product View on Hacker NewsJun 25, 2021

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, coding · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

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