Quality Assessment

The assessment of Quality of Experience (QoE) has become an essential research area in various domains, including telecommunications, multimedia services, user interfaces, and interactive applications. Understanding and evaluating the subjective experiences of users is crucial for optimizing systems, enhancing user satisfaction, and improving the overall user-centric design.

The primary objective of this research activity is to explore and advance the methodologies for assessing QoE across different domains and media (e.g., 2D, 3D, multiview, VR, AR). By examining various factors that influence user experience, this research aims to develop reliable and effective assessment techniques to measure subjective user perceptions accurately.

Key research areas include:

• User-Centric QoE Evaluation: this research activity focuses on investigating user-centric evaluation methods that capture the subjective experiences of users. These methods include the design and implementation of subjective tests to collect data on users’ perceptions, preferences, and satisfaction levels.
• Objective QoE Metrics: this research activity explores the development of objective QoE metrics. These metrics employ technical measurements and algorithms to assess factors such as video quality, audio quality, response time, latency, and network performance. By combining objective metrics with subjective evaluation, a comprehensive understanding of QoE can be achieved.
• Context-Aware QoE Assessment: context plays a vital role in determining user perception and satisfaction. This research activity investigates the impact of various contextual factors, such as user location, network conditions, device characteristics, and content types, on QoE. By considering the context, we aim to develop context-aware assessment models that provide a more accurate representation of user experience.

Recent publications:

@article{nokey,

title = {No-Reference Light Field Image Quality Assessment Exploiting Saliency},

author = {Kamal Lamichhane and Michael Neri and Federica Battisti and Pradip Paudyal and Marco Carli},

url = {https://ieeexplore.ieee.org/document/10091190},

doi = {10.1109/TBC.2023.3242150},

issn = {0018-9316},

year  = {2023},

date = {2023-04-03},

urldate = {2023-04-03},

journal = {IEEE Transactions on Broadcasting},

pages = {1-11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lamichhane2023,

title = {A CNN-based no reference image quality metric exploiting content saliency},

author = {Kamal Lamichhane and Marco Carli and Federica Battisti},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Signal Processing: Image Communication},

volume = {111},

pages = {116899},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@inproceedings{Baldoni_ISPA_2023,

title = {An Ablation Study on 360-Degree Saliency Estimation},

author = {Sara Baldoni and Ortisa Poci and Giancarlo Calvagno and Federica Battisti},

url = {https://ieeexplore.ieee.org/abstract/document/10279300},

doi = {10.1109/ISPA58351.2023.10279300},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 International Symposium on Image and Signal Processing and Analysis (ISPA)},

pages = {1-6},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}