EDedflsafoiewq·Jul 14, 2026Submission
1 points · Open discussionBinomialLLC
basis_universal
C++2019–2026
BinomialLLC / basis_universal
basis_universal
Basis Universal GPU Texture Codec
15Distinct people
17Mentions
0Explicit recommenders
3.1KGitHub stars
What to know before exploring
What it is
Basis Universal GPU Texture Codec
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
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Also in these discussions
- binomialllc/crunch · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year17 dated mentions
Observed mentions: 2019-05-22 21:47:58 UTC to 2026-07-14 09:34:53 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 4 | 3 | 1 |
What Hacker News says
17 sourcesI feel like it's a pretty weird omission taht basis_universal isn't mentioned at all? Can also target Khronos's ktx2 too.
https://github.com/BinomialLLC/basis_universal
In Hardware Image CompressionIs this similar to Basis ? https://github.com/BinomialLLC/basis_universal
In OpenZL: An open source format-aware compression frameworkHow does this compare to Basis Universal encoding?
https://github.com/BinomialLLC/basis_universal
In Spark Texture Compression 1.2The author of the OP also develops Basis, which is designed to take advantage of GPU texture compression formats. GPU formats are efficient in terms of memory space and bandwidth usage, but they have a fixed bits-per-pixel so they're not the most efficient for storage and transmission. There's also a rift between which formats are supported on desktop and mobile GPUs. Basis layers on a second level of variable-bitrate compression which is designed to be efficient to transcode to various compressed GPU formats (for both desktop and mobile) on the fly.
https://github.com/BinomialLLC/basis_universal
Delivering images in a GPU-friendly manner on the web could be a massive memory saver (PNG/JPGs end up being 32 bits-per-pixel in memory, while GPU formats are usually 4bpp or 8bpp) but web standards haven't caught up yet, for now the only way to
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display these formats on the web is inside a WebGL context. It works, but it's a lot of machinery just to show a picture.
Erm, aren't both WebP and PNG rather useless for games? How do you convert those formats on the fly into one of the hardware-compressed texture formats consumed by the GPU (like BCx, ETC or ASTC)? If you're decoding PNG or WebP to one of the linear texture formats, you're wasting a ton of GPU memory and texture sampling bandwidth.
(these are probably better alternatives: https://github.com/BinomialLLC/basis_universal, or http://www.radgametools.com/oodletexture.htm)
In Hello, PNG> video game assets are often enormous in size because developers refuse to implement even very basic compression because of the supposed performance impact
This claim needs some real world evidence to back it up (and usually it's not about a performance impact, but instead a perceived image quality impact).
IME a lot of care is taken for compressing asset data, and if there would be a chance to reduce the asset size by another few percent without losing too much detail (that's the important part), it would be done. In the end, textures need to end up in memory as one of the GPU compatible hardware compressed texture formats (e.g. BCx) - which is important not only for reducing memory usage, but mainly for increasing texture sampling performance on the GPU (reduced memory bandwidth and better cache locality when reading texture data from
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GPU memory).
Those hardware-compressed texture formats rule out any of the popular image formats like JPEG, PNG etc... that might have a better overall compression, but are hard to decode on the fly to one of the hardware texture formats (but there are now alternatives like https://github.com/BinomialLLC/basis_universal), but even a generic lossless compressor (even good old zip) on top of BCx is already a good start.
Im interested to see what image format specific compressors go gpu. JpegXL, AVIF, WebP... who wants to show up & throw down? Or even just fastpng?
Meanwhile we dont really hear or regard many of the gpu-oriented compression techs. TIL Basis/KTX2 is itself zstd compressed (formerly LZ apparently?). https://github.com/BinomialLLC/basis_universal
In Brotli-G: A GPU compression/decompression standard for digital assetsHi HN, I built a website for tracking an attempt by two climbers to set the fastest known time (FKT) on a well-known list of 247 peaks in the Sierra Nevada.
While I hope you'll find the whole site interesting[1], the part most likely of interest to this community is the interactive 3D live tracking (the main link) and activity playback[2]. Here's a post describing its capabilities/controls[3].
I rolled my own 3D terrain renderer based on open aerial data (USDA NAIP), elevation data (USGS NED), and named locations (USGS BGN). A few points of difference from what you might see from Google, Mapbox, or Cesium:
- Fixed scene size. The scene is only big enough to capture the area of interest (the track) plus some contextual buffer.
- No incremental loading. This was the motivation behind the fixed scene size… once the imagery and elevation mode
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l are loaded, the scene runs at 60fps on just about any device. I want it to feel _fast_ and terrain, imagery, or feature data loading and unloading breaks that feeling.
- Compressed textures for imagery. In order to successfully load some of the larger scenes and to reduce imagery file sizes, I use basis_universal[4] texture compression. It's perfect for this sort of imagery: the reduced quality isn't too discernable and you get 8:1 reduction in on-GPU memory usage against standard RGBA images.
- Hand coded DEM compression. I hand rolled a DEM compression algorithm based on the ideas behind QOI[5]. It turned out to hardly be necessary because the poor quality of DEMs available in the Sierra Nevada make their size immaterial compared to imagery. That said, it was a successful effort insofar as it achieved better than bzip2 compression with O(n) encode/decode times.
- 3D-scene rectified photos. For the first third of the project I was manually hand rectifying photos into the 3D activity view[2], but, in the end, this was taking too much time day-to-day.
This has been my passion project for the last six months or so and, as someone who has climbed 89 of these peaks over 20+ years, it has been a real joy chronicling these guys' amazing efforts.
I hope you enjoy it. Any and all feedback welcome… thanks.
[1] - https://www.sps2022.com
[2] - https://www.sps2022.com/activity/7016024405, let it load then hit the play button!
[3] - https://www.sps2022.com/post/3d
[4] - https://github.com/BinomialLLC/basis_universal
[5] - https://qoiformat.org/
I wouldn't call Oodle Texture a hack. But there's also https://github.com/BinomialLLC/crunch and https://github.com/BinomialLLC/basis_universal. The latter has the advantage that it can be decoded to multiple different compressed texture formats, so that all GPUs can be supported from a single file (different GPUs support different formats so you can't necessarily send the same compressed texture to any GPU).
In The QOI File Format SpecificationThey are used widely in games. But, not so much elsewhere. Non-game-app designers get upset by the artifacts. I've shipped games using them for UI. We were hapy to tolerate the artifacts in exchange for the 8X memory usage & memory bandwidth reduction (RGBA8888 vs. PVTRC4).
However, I've heard that Netflix was one of the first sponsors of https://github.com/BinomialLLC/basis_universal They are shipping innumerable images to a huge variety of underpowered set top boxes. Totally worth investing in a giant-leap memory optimization.
In John Carmack on JPEGWhy is the demo link towards the bottom of the post pointing to the Basis Universal repository (which is a texture compressor)?
https://github.com/BinomialLLC/basis_universal/tree/master/w...
Copy-pasta error, or did they run the post through Lyra? ;)
In Google open-sources the Lyra audio codecFrom their readme at
https://github.com/binomialLLC/basis_universal
Basis Universal Supercompressed GPU Texture Codec
Basis Universal is a "supercompressed" GPU texture compression system that outputs a highly compressed intermediate file format (.basis) that can be quickly transcoded to a very wide variety of GPU compressed and uncompressed pixel formats: ASTC 4x4 L/LA/RGB/RGBA, PVRTC1 4bpp RGB/RGBA, PVRTC2 RGB/RGBA, BC7 mode 6 RGB, BC7 mode 5 RGB/RGBA, BC1-5 RGB/RGBA/X/XY, ETC1 RGB, ETC2 RGBA, ATC RGB/RGBA, ETC2 EAC R11 and RG11, FXT1 RGB, and uncompressed raster image formats 8888/565/4444.
In Google and Binomial partner to open source high quality Basis UniversalShowing the 15 most recent of 17 sources. Enable JavaScript to search and browse every indexed mention.