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

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No-reference stereoscopic image quality assessment based on binocular collaboration.

Hanling Wang¹, Xiao Ke¹, Wenzhong Guo¹

¹Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350116, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 28, 2024

Summary

This study introduces a novel no-reference stereoscopic image quality assessment (NR-SIQA) method. It uses a neural network and saliency-guided cropping to accurately predict image quality without original references.

Keywords:

Image processing Neural networks No-reference image quality assessment Stereoscopic image quality

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Area of Science:

Computer Vision
Image Processing
Human Visual System

Background:

Assessing stereoscopic image quality (SIQA) is challenging due to binocular vision complexities and inter-view disparities.
Existing methods may exhibit bias in quality prediction for multi-distorted images.

Purpose of the Study:

To develop a no-reference SIQA method addressing quality prediction bias.
To investigate human visual cortex processing for improved image quality evaluation.

Main Methods:

Proposed an end-to-end neural network for NR-SIQA.
Developed a saliency-guided picture patch generation algorithm fusing left and right views for image cropping.

Main Results:

The novel method outperforms state-of-the-art NR-SIQA metrics on LIVE 3D and WIVC 3D databases.
Achieved excellent performance on specific noise metrics.
Demonstrated model generalization capabilities.

Conclusions:

The proposed NR-SIQA method effectively evaluates stereoscopic image quality without reference images.
The saliency-guided approach enhances accuracy and robustness in quality prediction.