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Related Experiment Video

Updated: Feb 14, 2026

How to Create and Use Binocular Rivalry

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

Binocular Rivalry and Fusion-Inspired Hierarchical Complementary Ensemble for No-Reference Stereoscopic Image Quality

Yiling Tang¹, Shunliang Jiang¹, Shaoping Xu¹

¹School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China.

Sensors (Basel, Switzerland)

|February 13, 2026

Summary

This summary is machine-generated.

This study introduces a new framework for assessing stereoscopic image quality without references. The Multi-Stage Complementary Ensemble (MSCE) framework improves prediction accuracy, especially with complex distortions.

Keywords:

Swin Transformer asymmetric distortion binocular fusion binocular rivalry multi-stage complementary ensemble stereoscopic image quality assessment

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Last Updated: Feb 14, 2026

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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Area of Science:

Computer Vision
Image Processing
Human Visual System Modeling

Background:

No-reference stereoscopic image quality assessment (NR-SIQA) is challenging due to binocular rivalry and fusion complexities.
Asymmetric distortions in stereoscopic images further complicate quality evaluation.

Purpose of the Study:

To propose a novel framework, Multi-Stage Complementary Ensemble (MSCE), for NR-SIQA.
To enhance the dynamic regulation of binocular feature fusion and improve prediction consistency.

Main Methods:

Developed an Adaptive Selective Propagation (ASP) strategy within a hierarchical Transformer architecture.
Simulated human visual system transitions from rivalry to fusion using nonlinear gain control.
Integrated low-level, mid-level, and high-level features using a Hierarchical Complementary Fusion (HCF) module.

Main Results:

The MSCE framework achieved state-of-the-art performance on four benchmark datasets.
Demonstrated superior prediction consistency under complex asymmetric distortions.
Validated the effectiveness of the ASP strategy and HCF module in handling binocular discrepancies.

Conclusions:

The MSCE framework offers a significant advancement in NR-SIQA, particularly for challenging asymmetric distortions.
The proposed methods effectively model human visual perception for more accurate image quality assessment.
MSCE provides a robust solution for evaluating stereoscopic image quality in real-world applications.