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#compression
New blog post: reverse engineering the Fujitsu RELC hardware compression used in Samsung M7MU cameras.
Thanks to @nina_kali_nina and @IgorSkochinsky for inspiration and support!
Post: https://op-co.de/blog/posts/fujitsu_relc_compression/
yellow site: https://news.ycombinator.com/item?id=43896944
Muchos libros de texto en epub son bastante pesados, lo que hace imposible enviarlos al kindle desde el email por superar los 50MB.
He encontrado un script de bash que permite una compresión rápida y bastante aceptable. En mi caso ha funcionado perfectamente con calidad y redimensionado del 60%, tal y como se ve en la imagen. Ha pasado un epub de un libro de más de mil páginas con más de 200 imágenes de 74 a 37MB.
Script original https://ebooks.stackexchange.com/a/7208
P.S. I got down from 33GB to 22GB in one mailbox. Individual mail differences in compression between level 3 and level 22 did not exceed 0.05% #zstd #mail #compression #dovecot
2/2 I also tested the impact of compression levels and I can say, it is not worth the CPU to go beyond the default #zstd level of 3. Base64 encoded mail attachments do not compress very well. This way it is fast and reliable.
To move the mailbox to the new server, I just resynced the newly creates maildir:username@domain.tld and configured the new dovecot similarly for compression and access.
Voila!
1/2 I had to move my #mail server and wanted to use #compression. The docs are, as often, not really helpful. Ultimately, after some testing the best way to do it is, as in a side note in the #dovecot docs, to use "doveadm". First configure to use zlib globally in mail_plugins and configure it to use zstd with the default level. Then just do
doveadm -v -o maildir_copy_with_hardlinks=no backup -u username maildir:user@domain.tld
5 Ways to Reduce HTML File Size on Your Website, by @debugbear.com:
Compute Bigger with Cat2Cloud Compression! 
In this third video, we explore lazy expressions 
—a compression-native tool to optimize operations 
on your data using the Cat2Cloud web client. 
With lazy expressions, you can decompress and compute data only when needed, saving time and resources! 
Try it out here: https://cat2.cloud/demo
Learn more at https://ironarray.io/cat2cloud 
#compression #datascience #ironArray #data
Missed PyData Global 2024? No worries! You can now catch with our recorded session on YouTube. 
In my tutorial, I covered:
The basics of an NDArray object in Blosc2
How to leverage lazy expressions for efficient computation
Efficiently computing UDFs within the Blosc2 engine
Accessing your data from the network: a gentle intro to Caterva2
Watch the video here: https://www.youtube.com/watch?v=aR-i_a3nGx0
Don't miss out—learn, explore, and level up your data skills!
Thinking about #Compression for #RetroComputing #PixelArt on monochrome (1bpp) images...
Obviously, Huffman does nothing. Obviously, RLE and LZ applied to bytes only catch repetitions if they're apart by a multiple of 8 pixels, i.e. rarely outside of large uniform areas.
But then, what works? Classic algorithms applied at the bit level? On pairs of bits?
Excited to announce the release of the second cat2cloud introductory video! 
Our software optimises file transfer and access for server-hosted data 
. cat2cloud's compression-first framework enables users to minimise transfer times and storage requirements .
In this video, you can see how easy and quick it is to manage file storage using the ubiquitous jupyter notebook format 
!
Find out more (and see the first video) at https://ironarray.io/cat2cloud!
#API #datascience #compression #jupyter
> Compression Dictionary Transport is a way of using a shared compression dictionary to dramatically reduce the transport size of HTTP responses.
https://developer.mozilla.org/en-US/docs/Web/HTTP/Guides/Compression_dictionary_transport
I am looking for a professional #mixing #electronicmusic tutorial (#gainstaging, #eq, #compression, #stereopositioning, #delay, #reverb, #chorus, etc.).
Can anyone recommend something?
Does not need to be free. Does not have to be #bitwigstudio specific.
#musicproduction
This is Internet Gold.
> "... it is important to note that the compression algorithm used by lzip only discards the unimportant data. And if it was unimportant before, what makes it so important now? Huh? In fact, many users may find that compressing their entire file system and then restoring it will be a good way to learn what is truly important."
https://web.archive.org/web/20010608045914/http://lzip.sourceforge.net/faq.html
Brand new PEP by @emmatyping to add Zstandard to the standard library:
https://peps.python.org/pep-0784/
Will it make it in to 3.14 before the feature freeze on 2025-05-06? It'll be close but it's possible!
The PEP also suggests namespacing the other compression libraries lzma, bz2 and zlib, with a 10-year deprecation for the old names.
Join the discussion to give your support, suggestions or feedback:
https://discuss.python.org/t/pep-784-adding-zstandard-to-the-standard-library/87377
**Exciting News!** After 15 years of developing #Blosc/#Blosc2, we're thrilled to announce the beta program for Cat2Cloud! 
- 
Share complex data securely and effortlessly
- Access to the best compression algorithms available
- Perform advanced computations directly in the cloud
...and more!
https://ironarray.io/cat2cloud
Join our beta program today and be among the first to experience the power of Cat2Cloud!
#DataScience #Compression #SaaS #CloudComputing #BetaProgram
Share Data Faster!
Salut, je cherche une app mobile libre pour compresser des images jpg par lot. Une appli à me recommander ?
#help #android #mobile #photo #app# #fdroid #compression #manipulationImage
Scientists are storing light we cannot see in formats meant for human eyes - Imagine working with special cameras that capture light your eyes can't ev... - https://arstechnica.com/science/2025/03/scientists-are-storing-light-we-cannot-see-in-formats-meant-for-human-eyes/ #multispectrumimages #imagecompression #spectralimaging #imageformats #compression #datascience #science #biz #jpegxl #jpeg
Updated article on Blosc2: Compute with TB-sized datasets on your own hardware, within human timeframes!
Highlights:
Outperforms NumPy by 10x ~ 100x for large computations
Maintains performance with datasets far exceeding physical memory Integrates seamlessly with the Python data science ecosystem
Works both in-memory and on-disk with minimal performance differences
Read more: https://ironarray.io/blog/compute-bigger
