libjpeg-turbo
NumPy
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libjpeg-turbo | NumPy | |
---|---|---|
15 | 272 | |
3,582 | 26,360 | |
1.3% | 1.9% | |
8.4 | 10.0 | |
15 days ago | 1 day ago | |
C | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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libjpeg-turbo
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Jpegli: A New JPEG Coding Library
> all decoders will render the same pixels
Not true. Even just within libjpeg, there are three different IDCT implementations (jidctflt.c, jidctfst.c, jidctint.c) and they produce different pixels (it's a classic speed vs quality trade-off). It's spec-compliant to choose any of those.
A few years ago, in libjpeg-turbo, they changed the smoothing kernel used for decoding (incomplete) progressive JPEGs, from a 3x3 window to 5x5. This meant the decoder produced different pixels, but again, that's still valid:
https://github.com/libjpeg-turbo/libjpeg-turbo/commit/6d91e9...
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My personal C coding style as of late 2023
Last vestiges of this fact AFAIK were libjpeg, which had a macro NEED_SHORT_EXTERNAL_NAMES that shortens all public identifiers to have unique 6-letter-long prefixes. Libjpeg-turbo nowadays has removed them though [1].
[1] https://github.com/libjpeg-turbo/libjpeg-turbo/commit/52ded8...
- Libjpeg-Turbo 3.0.0
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Why there may never be a libjpeg-turbo 3.1
While I think the move to safer code through Rust and other alternatives is a nice breath of fresh air, I doubt you can get these kinds of optimization without using unsafe code in Rust. These optimized implementations often require some kind of safety-bypassing memory modifications to work as efficiently ad they do.
There's a reason https://github.com/libjpeg-turbo/libjpeg-turbo/tree/main/sim... is filled with assembly files with conditional loading.
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Learn x86-64 assembly by writing a GUI from scratch
Sure. You'll see it very often in codec implementations. From rav1e, a fast AV1 encoder mostly written in Rust: https://github.com/xiph/rav1e/tree/master/src/x86
Large portions of the algorithm have been translated into assembly for ARM and x86. Shaving even a couple percent off something like motion compensation search will add up to meaningful gains.
Or the current reference implementation of JPEG: https://github.com/libjpeg-turbo/libjpeg-turbo/tree/main/sim...
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Announcing zune-jpeg: Rust's fastest JPEG decoder
zune-jpeg is 1.5x to 2x faster than jpeg-decoder and is on par with libjpeg-turbo.
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JDK 21 - Image Performance Improvements
This is interesting from the standpoint of how new JVM features can be used to improve performance (what I presume the article's main purpose to have been), but the image processing improvement itself isn't head-turning. Also, we've found that libjpeg-turbo (https://libjpeg-turbo.org/) is ~5x (IIRC, can re-run my JMH benchmark if anyone wants me to) as fast for decoding JPEGs as ImageIO, so we wouldn't even benefit from this change in 21 much.
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Convenient CPU feature detection and dispatch in the Magnum Engine
libjpeg-turbo: https://github.com/libjpeg-turbo/libjpeg-turbo/blob/main/simd/x86_64/jsimdcpu.asm
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Implementing SVE2 for Open Source Project
libjpeg-turbo
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How to go about implementing file encoding [Question]
For all but the simplest formats (basically BMP), the difficulty of implementing encoding/decoding from scratch is significant - well beyond a beginner's ability, and challenging/time-consuming even for senior developers. So, libraries are used in practice - e.g. libpng and libjpeg-turbo.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
ImageMagick - 🧙♂️ ImageMagick 7
SymPy - A computer algebra system written in pure Python
libwebp - Mirror only. Please do not send pull requests. See https://chromium.googlesource.com/webm/libwebp/+/HEAD/CONTRIBUTING.md.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
orion - Usable, easy and safe pure-Rust crypto
blaze - NumPy and Pandas interface to Big Data
bloom - The simplest way to de-Google your life and business: Inbox, Calendar, Files, Contacts & much more
SciPy - SciPy library main repository
virtualgl - Main VirtualGL repository
Numba - NumPy aware dynamic Python compiler using LLVM
Rustup - The Rust toolchain installer
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).