How to Install and Uninstall butteraugli Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 18,2024

1. Install "butteraugli" package

Please follow the steps below to install butteraugli on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                butteraugli
            
                    
    
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2. Uninstall "butteraugli" package

This tutorial shows how to uninstall butteraugli on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                butteraugli
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the butteraugli package on Ubuntu 20.10 (Groovy Gorilla)

Package: butteraugli
Architecture: amd64
Version: 0~20170116-3build1
Priority: optional
Section: universe/graphics
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian PhotoTools Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 88
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libpng16-16 (>= 1.6.2-1), libstdc++6 (>= 4.1.1)
Filename: pool/universe/b/butteraugli/butteraugli_0~20170116-3build1_amd64.deb
Size: 33052
MD5sum: 891432630da5b259300e06dd920daaad
SHA1: c25609dc773d1fa0d0e112db65f101edc21d1131
SHA256: 39b9207cf3da9b4846c556dbf1921ffe3c81cc609a8eee26785fc3214abcea7d
SHA512: 3fdbe8dcea93a2c7d553985360fd1830c3166d8f42dc587ced0f7fed577d993736b538fb8e42e76e4cd76c1bdb00aaf133dcec2a05ffe089124fbe5d8a479a4a
Homepage: https://github.com/google/butteraugli
Description-en: measuring perceived differences between images
Butteraugli is a project that estimates the psychovisual similarity of
two images. It gives a score for the images that is reliable in the
domain of barely noticeable differences. Butteraugli not only gives a
scalar score, but also computes a spatial map of the level of
differences.
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One of the main motivations for this project is the statistical
differences in location and density of different color receptors,
particularly the low density of blue cones in the fovea. Another
motivation comes from more accurate modeling of ganglion cells,
particularly the frequency space inhibition.
Description-md5: 870cf0dbd8cfe04bf185f1d5bac9be11