Multi-Distortion Artificial Intelligence-based Observers (MD-AIOs)

This software includes the AI Observers models (trained Matlab NNs)
presented in the paper

Multiple Image Distortion DNN Modeling Individual Subject Quality
Assessment

by Lohic Fotio Tiotsop, Antonio Servetti, Marcus Barkowsky, Peter Pocta,
Glenn Van Wallendael, Enrico Masala,
ACM Transactions on Multimedia Computing, Communications, and
Applications (ACM TOMM), vol. 20, Jun 2024, pp. 1-27, DOI:
10.1145/3664198 (ISSN: 1551-6857) (freely available to anyone as open
access)

The software is provided free of charge, see LICENSE.txt file for
further conditions.
We kindly ask anyone using this software to cite the previous paper.

NOTE: Due to its large size (1.5+ GB), the full set of data files (.mat
files) must be downloaded from https://media.polito.it/MD-AIOs

How to run the code

May 2, 2024

The present instructions are provided with the software in order to
explain how to run it.

Please note that to run the provided software, it is recommended to have
installed MATLAB R2019b or later Versions.

The folder contains:

-   20 .mat files containing the weights and the architecture of the
    trained Deep Neural Networks (DNNs) i.e., the JPEGResNet50 and the
    19 AIOs (to be downloaded from https://media.polito.it/MD-AIOs).

-   the MATLAB Simulate_AI_subjective_test.m that provides the software
    for simulating a subjective experiment with the AIOs.

-   the MATLAB script get_quality_MDResNet50.m that provides the
    software for computing the Visual quality of an images impaired by
    Gaussian Noise, Gaussian blur, JPEG and JPG2000 compression
    artefacts using the MDResNet50.

-   a folder called Test_folder containing few images on which the
    software can be tested.

To run Simulate_AI_subjective_test.m or get_quality_MDResNet50.m on a
set of images, first prepare a folder containing the Images whose
quality should be evaluated, then insert it in the folder that contains
the 19 DNNs modeling the AIOs, the MDResNet50,
Simulate_AI_subjective_test.m and get_quality_MDResNet50.m. Open the
script Simulate_AI_subjective_test.m or get_quality_MDResNet50.m
depending on what you want to do, update the variable named “imgfolder”
by inserting the name of the folder containing the images whose quality
should be evaluated, then simply run the script.

In case of get_quality_MDResNet50.m, a csv file named MDResNet50_MOS.csv
reporting the estimation of the MOS of each image will be generated and
saved in the folder that contains the scripts.

In case of Simulate_AI_subjective_test.m, 21 csv files are generated in
the folder that contains the scripts: The file MOS_AI.csv that reports
the MOS computed from the AIOs opinion scores; the file
AI_opinin_scores.csv containing the AIOs opinion Scores; and 19 other
files, one for each AIOs, reporting the probability of choosing each of
the five possible opinion scores i.e., “Bad”, “Poor”, “Fair”, “Good” and
“Excellent” for every evaluated image.

Please send an email to lohic.fotiotiotsop@polito.it for any further
questions.