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Viewport-Based CNN: A Multi-Task Approach for Assessing 360° Video Quality.

Mai Xu, Lai Jiang, Chen Li

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 5, 2020

Summary

This summary is machine-generated.

This study introduces a novel viewport-based approach for assessing 360° video quality. By analyzing head and eye movements, the method accurately predicts visual quality, outperforming existing techniques.

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Area of Science:

Computer Vision
Multimedia Systems

Background:

Current 360° video quality assessment methods overlook user viewport access.
User experience is dictated by head and eye movements within viewports.

Purpose of the Study:

To develop a viewport-based Visual Quality Assessment (VQA) approach for 360° video.
To establish a large-scale dataset for 360° video VQA, including head movement (HM) and eye movement (EM) data.

Main Methods:

A two-staged multi-task learning framework using a viewport-based Convolutional Neural Network (V-CNN).
The V-CNN incorporates a viewport proposal network (VP-net) and a viewport quality network (VQ-net).
Auxiliary tasks include camera motion detection, viewport proposal, and saliency prediction.

Main Results:

The proposed V-CNN significantly improves VQA performance for 360° video.
The model demonstrates effectiveness in auxiliary tasks like camera motion detection and saliency prediction.
Subjective quality is linked to camera motion, viewport position, and in-viewport saliency.

Conclusions:

Viewport-based VQA is crucial for accurate 360° video quality assessment.
The V-CNN framework offers a robust solution for 360° video VQA and related tasks.
The VQA-ODV dataset provides valuable insights into 360° video viewing behavior.