Myllena Prado

Texas, USA

I am a Ph.D. student in Computer Science at Texas State University, with a research focus on quality assessment of images and video. My work explores how machine learning, computer vision, and immersive technologies can be combined to improve the way we evaluate and experience visual media.

My work has been published in top venues such as IEEE ICIP and QoMEX, and I was honored with the Best Paper Award at WebMedia ‘22.

news

May 27, 2025	Paper ‘ViGBLiF: A Graph-Based Approach to No-Reference Light Field Image Quality Assessment’ accepted in QoMEX conference!
May 20, 2025	Paper ‘NASSBLIF: NO-REFERENCE LIGHT FIELD IMAGE QUALITY ASSESSMENT VIA NEIGHBORHOOD ATTENTION AND SCALE SWIN’ accepted in ICIP conference!
Jun 01, 2023	Started my PhD at Texas State University

latest posts

Aug 21, 2023	Light Fields

selected publications

NASSBLiF: No-Reference Light Field Image Quality Assessment Via Neighborhood Attention and Scale Swin

Myllena A. Prado and Mylène C. Q. Farias

In 2025 IEEE International Conference on Image Processing (ICIP), 2025

DOI Code
360RAT: A Tool for Annotating Regions of Interest in 360-degree Videos

Myllena Prado, Lucas Althoff, Sana Alamgeer, and 4 more authors

In Proceedings of the Brazilian Symposium on Multimedia and the Web, Curitiba, Brazil, 2022

Abs DOI Code

This paper introduces the software 360RAT as a tool for annotating regions of interest (RoIs) in 360-degree videos. These regions represent the portions of the video content that are important for telling a story throughout the video. We believe that this software is an invaluable tool for studying different aspects of 360-degree videos, including what viewers consider relevant and interesting to the experience. As part of this work, we conducted a subjective experiment in which 9 human observers used the proposed software to annotate 11 360-degree videos. As a result, we created a dataset containing a set of annotated 360-degree videos, i.e., videos with marked RoIs and their semantic classification. We present a simple analysis of the annotations gathered with the experiment for a subset of the videos. We noticed that there is a higher agreement of annotations among participants for videos with fewer objects. We also compared the RoI maps with saliency maps computed with the Cube Padding saliency model. We found a strong correlation between RoI maps and computed saliency models, indicating a link between the annotated RoI and the saliency properties of the content.