Public code of software Robust Curvelet IQA - Published in WVC2018 - Ilhéus BA - Brasil
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Robust Curvelet IQA release version

Public code of software Robust Curvelet IQA described in article:

Robust statistics and no-reference image quality assessment in Curvelet domain.

Published in XIV Workshop de Visão Computacional (WVC 2018), Ilhéus BA, Brazil.

It is available for consultation at,

arxiv no double blind version at,


Because 'pyct' this software run under python 2.7;

List of packeges to run Rcurvelet_Features: skimage, sklearn, pandas, numpy, sys, scipy and pyct.

Only 'pyct' don't install by pip install, easy install or anaconda repo.

To install pyct use the repository:

Running the examples

Features Generator:

To make a train features type:

$ python path/to/input_image.jpg path/to/output_file.csv

To make a test features:

$ python path/to/input_image.jpg path/to/output_file.csv image_class image_survey_score

Score Q Generator:

Watch out for models in models folder.

This module uses trained models. The models were trained with classes jpge, jp2k, Gaussian white noise and Gaussin Blur of data sets Live IQA, TID2013 and CSIQ.

The name of the models follows a fixed structure: a) Regressor models name is: regressor_class_DEGRADATION_model.pkl b) Scale models name is: scale_class_DEGRADATION_model.pkl

To make a score:

$ python input.csv #To show

$ python input.csv score.csv #To save output file.

Note: input.csv can be a multiline file.

Model Generator:

Will appear soon

Usefull script for batch of images

Use a shell script "" to make a scores for a batch of images.


Robust Curvelet IQA 2018.01 For the versions available, see the


Ramón Giostri Campos


This project is licensed under terms of the GNU General Public License version 3 - see the [] file for details


For the professor Evandro Ottoni Teatini Salles for guidance in the works.