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Subjective and Objective Quality Assessment of Rendered Human Avatar Videos in Virtual Reality.

Yu-Chih Chen, Avinab Saha, Alexandre Chapiro

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 2, 2024
    PubMed
    Summary

    This study introduces a new dataset for evaluating digital human avatar video quality in virtual and augmented reality. The dataset aids in developing better video quality assessment models for immersive experiences.

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    Area of Science:

    • Computer Vision
    • Immersive Technologies
    • Perceptual Quality Assessment

    Background:

    • Digital human avatars are increasingly used in virtual reality (VR) and augmented reality (AR) systems, necessitating high-quality video transmission.
    • Internet transmission can introduce visual artifacts, impairing the perceived quality of compressed avatar videos.
    • Existing video quality assessment (VQA) models are insufficient for specialized human avatar video analysis due to limited datasets.

    Purpose of the Study:

    • To address the lack of specialized datasets for human avatar video quality assessment.
    • To create a comprehensive database of rendered human avatar videos with human perceptual quality judgments.
    • To evaluate the performance of existing and novel VQA models on this specific type of content.

    Main Methods:

    • Development of the LIVE-Meta Rendered Human Avatar VQA Database, featuring 720 videos with 20 encoding parameter combinations.
    • Collection of human perceptual quality judgments using six degrees of freedom VR headsets.
    • Benchmarking of state-of-the-art Full Reference and No Reference VQA models, including a new model named HoloQA.

    Main Results:

    • The newly created database provides a valuable resource for VQA research on human avatars.
    • Performance analysis of various VQA models reveals their effectiveness and limitations for avatar video quality prediction.
    • The HoloQA model is introduced and evaluated within the context of this specialized dataset.

    Conclusions:

    • The LIVE-Meta database is crucial for advancing VQA models tailored to human avatar streaming in VR/AR.
    • Accurate VQA models are essential for optimizing compression strategies and ensuring high perceptual quality in immersive applications.
    • Public release of the database metadata supports further research and development in the field.