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A Spatial-Temporal Video Quality Assessment Method via Comprehensive HVS Simulation.

Ao-Xiang Zhang, Yuan-Gen Wang, Weixuan Tang

IEEE Transactions on Cybernetics

|December 25, 2023

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This study introduces HVS-5M, a new video quality assessment method that simulates five human visual system (HVS) characteristics for more accurate results. HVS-5M significantly improves upon existing methods in evaluating video quality.

Area of Science:

Computer Vision
Signal Processing
Human-Computer Interaction

Background:

Video quality assessment (VQA) is crucial for video service providers.
Deep neural networks have advanced VQA, but limitations exist in modeling the human visual system (HVS).
Existing VQA methods often incompletely model HVS characteristics and lack interconnections.

Purpose of the Study:

To propose a novel spatial-temporal VQA method, HVS-5M, inspired by the HVS.
To address limitations in current VQA by simulating multiple HVS characteristics and their connections.
To enhance the accuracy and comprehensiveness of video quality evaluation.

Main Methods:

Developed HVS-5M, a VQA method with five modules simulating HVS characteristics.
Spatial domain modules: visual saliency, content-dependency, and edge masking, weighted by saliency maps.

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Temporal domain modules: motion perception and temporal hysteresis, simulating memory mechanisms for feature fusion.

Main Results:

HVS-5M demonstrated superior performance compared to state-of-the-art VQA methods.
Extensive experiments validated the effectiveness of the proposed method.
Ablation studies confirmed the contribution of each module to the overall performance.

Conclusions:

The proposed HVS-5M method effectively simulates key HVS characteristics for improved video quality assessment.
The bio-inspired connection among modules enhances the comprehensive evaluation of video quality.
HVS-5M represents a significant advancement in spatial-temporal VQA.