How to Install and Uninstall libxbrz-1_8 Package on openSuSE Tumbleweed
Last updated: November 26,2024
1. Install "libxbrz-1_8" package
Here is a brief guide to show you how to install libxbrz-1_8 on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
libxbrz-1_8
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2. Uninstall "libxbrz-1_8" package
This guide covers the steps necessary to uninstall libxbrz-1_8 on openSuSE Tumbleweed:
$
sudo zypper remove
libxbrz-1_8
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3. Information about the libxbrz-1_8 package on openSuSE Tumbleweed
Information for package libxbrz-1_8:
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Repository : openSUSE-Tumbleweed-Oss
Name : libxbrz-1_8
Version : 1.8-2.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 342.4 KiB
Installed : No
Status : not installed
Source package : xbrz-1.8-2.5.src
Upstream URL : https://sourceforge.net/projects/xbrz/
Summary : Pattern recognition rule-based bitmap upscaler
Description :
The idea of xBR is to scale bitmaps using pattern recognition (like
HQx), but also uses a 2-stage set of interpolation rules, which
better handle more complex patterns such as antialiased lines and
curves. Background textures keep the sharp characteristics of the
original image rather than becoming blurry like with HQx.
xBRZ follows this idea, but has a different set of rules, focusing on
preserving small image features consisting of few pixels only, like
commonly used in faces.
------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libxbrz-1_8
Version : 1.8-2.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 342.4 KiB
Installed : No
Status : not installed
Source package : xbrz-1.8-2.5.src
Upstream URL : https://sourceforge.net/projects/xbrz/
Summary : Pattern recognition rule-based bitmap upscaler
Description :
The idea of xBR is to scale bitmaps using pattern recognition (like
HQx), but also uses a 2-stage set of interpolation rules, which
better handle more complex patterns such as antialiased lines and
curves. Background textures keep the sharp characteristics of the
original image rather than becoming blurry like with HQx.
xBRZ follows this idea, but has a different set of rules, focusing on
preserving small image features consisting of few pixels only, like
commonly used in faces.