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Related Experiment Video

Updated: Aug 28, 2025

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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Screen Content Video Quality Assessment Model Using Hybrid Spatiotemporal Features.

Huanqiang Zeng, Hailiang Huang, Junhui Hou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 20, 2022
    PubMed
    Summary

    A new hybrid spatiotemporal feature-based model (HSFM) accurately assesses screen content video (SCV) quality. This model effectively analyzes both screen and natural scenes for improved human visual system (HVS) perception.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Human-Computer Interaction

    Background:

    • Screen content videos (SCVs) present unique challenges for quality assessment due to their hybrid nature, combining screen-based and natural scene elements.
    • Existing video quality assessment (VQA) models often struggle to accurately capture the perceptual quality of SCVs as perceived by the human visual system (HVS).

    Purpose of the Study:

    • To design a full-reference video quality assessment (VQA) model specifically for screen content videos (SCVs).
    • To develop a model that effectively accounts for the distinct visual characteristics of screen and natural scenes within SCVs.
    • To improve the accuracy and perceptual relevance of VQA models for SCVs.

    Main Methods:

    • Extraction of screen and natural spatiotemporal features using the three-dimensional Laplacian of Gaussian (3D-LOG) filter and three-dimensional Natural Scene Statistics (3D-NSS).
    • Independent computation of feature similarities to generate distinct quality scores for screen and natural scene components.
    • An adaptive fusion scheme, utilizing local video activity, to combine screen and natural quality scores into a final VQA score.

    Main Results:

    • The proposed hybrid spatiotemporal feature-based model (HSFM) demonstrated superior performance in perceptual quality assessment of SCVs.
    • Experimental results on benchmark databases (SCVD and CSCVQ) confirmed HSFM's alignment with human perception.
    • HSFM outperformed various established and recent image and video quality assessment models.

    Conclusions:

    • The developed HSFM provides a more perceptually relevant VQA metric for screen content videos.
    • The hybrid approach effectively addresses the dual nature of SCVs by analyzing screen and natural scene characteristics separately and then fusing them.
    • The model's performance indicates its potential for practical applications in video compression and quality monitoring.