This repository contains the trained neural network (TensorFlow) models, associated with the paper "MTJND: Multi-Task Deep Learning Framework for Improved JND Prediction" Published in IEEE ICIP 2023. Paper The paper can be accessed through IEEEXplore. Preprint is available here. paper in Tensorflow. Requirements Tensorflow FFmpeg Dataset Our evaluation is conducted on VideoSet and MCL-JCI datasets. Usage Our pretrained models are capable of predicting JND values, and they can also be employed for training on a custom dataset. Note: The dataset used for training and testing should have such a structure. - rootdir/ - train/ - img#1 - img#2 - ... - JND-Levels.txt (a file containing the 3 JND levels per image: first column for the first JND, second column for the second JND, and third column for the third JND level) - valid/ - img#1 - img#2 - ... - JND-Levels.txt (a file containing the 3 JND levels per image: first column for the first JND, second column for the second JND, and third column for the third JND level) - test/ - img#1 - img#2 - ... Testing For prediction with MT_3LJND or MT_3LJND_VA, the following commands can be used. python3 MT_3LJND.py test --data_dir "Path-to-the-folder-containing-train,valid,and-test-subfolders/" --model_weights_path "Path-to-the-pretrained-model/model-name.h5" --result_path "Path-to-save-test-results/result.csv" For prediction with MT_1LJND_VA, the following commands can be used. python3 MT_1LJND_VA.py test --data_dir "Path-to-the-folder-containing-train,valid,and-test-subfolders/" --model_weights_path "Path-to-the-pretrained-model" --jnd_column int --result_path "Path-to-save-test-results/result.csv" For more information on the implemented code and training process, refer to the paper's GitHub page.