Abderrezzaq Sendjasni
Orcid: 0000-0002-6533-5675
According to our database1,
Abderrezzaq Sendjasni
authored at least 18 papers
between 2020 and 2025.
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Bibliography
2025
2024
A Two-Fold Patch Selection Approach for Improved 360-Degree Image Quality Assessment.
CoRR, 2024
Proceedings of the 26th IEEE International Workshop on Multimedia Signal Processing, 2024
Enhancing Perceptual Quality Assessment for 360-Degree Images Based on Adaptive Patch Labeling and Multi-Label Learning.
Proceedings of the IEEE International Conference on Image Processing, 2024
2023
Sensors, October, 2023
Objective and subjective quality assessment of 360-degree images. (Évaluation objective et subjective de la qualité des images à 360 degrés / Objektiv og subjektiv kvalitetsvurdering av 360-graders bilder).
PhD thesis, 2023
PW-360IQA: Perceptually-Weighted Multichannel CNN for Blind 360-Degree Image Quality Assessment.
Sensors, 2023
Adaptive Patch Labeling and Multi-Label Feature Selection for 360-Degree Image Quality Assessment.
Proceedings of the 25th IEEE International Workshop on Multimedia Signal Processing, 2023
Self Patch Labeling Using Quality Distribution Estimation for CNN-Based 360-IQA Training.
Proceedings of the IEEE International Conference on Image Processing, 2023
2022
Convolutional Neural Networks for Omnidirectional Image Quality Assessment: A Benchmark.
IEEE Trans. Circuits Syst. Video Technol., 2022
Patch-based CNN model for 360 image quality assessment with adaptive pooling strategies.
Proceedings of the Image Quality and System Performance XIX, 2022
SAL-360IQA: A Saliency Weighted Patch-Based CNN Model for 360-Degree Images Quality Assessment.
Proceedings of the IEEE International Conference on Multimedia and Expo Workshops, 2022
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022
Transfer Learning from Vision Transformers or ConvNets for 360-Degree Images Quality Assessmentƒ.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022
2021
Visual Scan-Path based Data-Augmentation for CNN-based 360-degree Image Quality Assessment.
Proceedings of the London Imaging Meeting 2021: Imaging for Deep Learning, 2021
Convolutional Neural Networks for Omnidirectional Image Quality Assessment: Pre-Trained or Re-Trained?
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021
Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd.
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021
2020
Proceedings of the Image Quality and System Performance XVII, Electronic Imaging 2020, 2020