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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Related Experiment Video

Updated: Aug 4, 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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Blind VQA on 360° Video via Progressively Learning From Pixels, Frames, and Video.

Li Yang, Mai Xu, Shengxi Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ProVQA, a novel blind visual quality assessment approach for 360° video. ProVQA mimics human perception by progressively learning from pixels, frames, and video, significantly improving quality assessment performance.

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    Area of Science:

    • Computer Vision
    • Multimedia Systems
    • Signal Processing

    Background:

    • Blind visual quality assessment (BVQA) is crucial for optimizing immersive 360° video experiences.
    • Current BVQA methods for 360° video do not align with the human perception paradigm of quality degradation.
    • Human perception of 360° video quality progresses from spatial distortion to motion artifacts, culminating in a video-level score.

    Purpose of the Study:

    • To propose a novel BVQA approach for 360° video that accounts for the progressive nature of human quality perception.
    • To develop a method that learns quality assessment progressively from pixels, frames, and the entire video.
    • To enhance the accuracy and effectiveness of quality assessment in immersive multimedia systems.

    Main Methods:

    • Developed ProVQA, a novel BVQA approach for 360° video.
    • Designed three sub-nets: Spherical Perception Aware Quality prediction (SPAQ), Motion Perception Aware Quality prediction (MPAQ), and Multi-Frame Temporal Non-Local (MFTN).
    • SPAQ models spatial degradation, MPAQ incorporates motion cues, and MFTN aggregates multi-frame information for final scoring.

    Main Results:

    • ProVQA significantly advances the state-of-the-art in BVQA for 360° video.
    • Experimental validation on two datasets demonstrates superior performance compared to existing methods.
    • The proposed approach effectively models progressive quality degradation as perceived by humans.

    Conclusions:

    • The ProVQA approach successfully addresses the limitations of existing BVQA methods for 360° video.
    • By mimicking human perception, ProVQA offers a more accurate and robust quality assessment framework.
    • The availability of the code facilitates further research and development in 360° video quality assessment.