TARZ — Texture Adaptive RANS

[panker1992]

Hello everyone,

During the past 5 months i have been working on several stuff.

Including Heavy deduplications engines, compression algorithms, precompression and other adjustments my archiver has already build in.

Today i am glad to present to you a new compression tool called, Tarz.


Tarz, is a lolz replacement which i created several weeks ago.
During my recent work on OGG/Opus data i found a way to see how modeling on data really works.
And therefore i decided to create something that can be used to compress Texture heavy Data.

I hope you enjoy using this tool.

In order for you to use this tool, ram is required because it has a build-in LRM.
Which fetches the data from a hefty distance away all while a pricing model creator is running for it.

Take notice that each Thread you pop, it will have its own memory, its own set of models and so on. so using multihtreading will heavily creeple the ratio on this tool.

[Masquerade]

Thanks
Been excited for this one...

[kj911]

Originally Posted by panker1992

Take notice that each Thread you pop, it will have its own memory, its own set of models and so on. so using multihtreading will heavily creeple the ratio on this tool.

Am I reading this correctly? If I use a single thread for compression, do I end up with a worse final compressed size compared to classic LZ algorithms—because there is no single-pass solid compression—meaning that running it on multiple threads would actually yield a better compression ratio? In the case of LOLZ/LZMA, does using a single thread produce the best results, while using multiple threads makes it faster but results in poorer performance (in terms of compression ratio)?

Are there any comparative test results regarding compression ratios for LOLZ/LZMA and Razor vs. Tarz?

[dixen]

Interesting project, thank you. It compresses very close to LOLZ, albeit more slowly. I would like to try it with settings equivalent to mtt1 tt2 mt8 mc 32. How can they be set?