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Related Experiment Video

Updated: Apr 16, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Quality Prediction of Embedded and Overlaid Text in User-Generated Visual Content.

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    Summary
    This summary is machine-generated.

    This study introduces new datasets and models for assessing text quality in user-generated videos. The research aims to improve the prediction of text legibility and overall video quality for better content analysis and recognition.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Signal Processing

    Background:

    • User-generated content (UGC), especially short-form videos, dominates online traffic.
    • Assessing the perceptual quality of UGC videos is crucial, yet text quality within them is understudied.
    • Text legibility impacts content perception, message conveyance, and automated recognition tasks like visual search.

    Purpose of the Study:

    • To address the lack of attention on text quality in short-form UGC videos.
    • To contribute to the psychophysics and computational modeling of embedded text perception.
    • To develop models for predicting text legibility and quality in images and videos.

    Main Methods:

    • Creation of two subjective datasets: LIVE-COCO Text Legibility (LIVE-COCO-TL) and LIVE-YouTube Text-in-Video Quality (LIVE-YT-TVQ).
    • LIVE-COCO-TL includes 74,440 text patches with legibility annotations.
    • LIVE-YT-TVQ comprises ~19K quality ratings on 405 videos and 641 text patches.
    • Development of models to predict text legibility, text quality, and a multi-task model for simultaneous overall and local text quality prediction.

    Main Results:

    • Development of predictive models for embedded/overlaid text legibility and quality.
    • A multi-task model was created to predict both overall video quality and local text quality.
    • The study provides novel datasets and computational models for text quality assessment in UGC.

    Conclusions:

    • The research advances the understanding and computational modeling of text quality in visual UGC.
    • The created datasets and models offer valuable resources for researchers and developers.
    • Accurate text quality prediction is essential for enhancing user experience and enabling advanced content analysis applications.