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PW-360IQA: Perceptually-Weighted Multichannel CNN for Blind 360-Degree Image Quality Assessment.

Sensors (Basel, Switzerland)·2023

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Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment.

Abderrezzaq Sendjasni¹, Mohamed-Chaker Larabi¹

¹CNRS, Université de Poitiers, XLIM, UMR 7252, 86073 Poitiers, France.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This study introduces an attention-aware deep-learning model for blind 360-degree image quality assessment (360-IQA). The model enhances accuracy and generalization, outperforming existing methods with reduced computational complexity.

Keywords:

360-degree images adaptive sampling convolutional neural networks image quality assessment saliency-based aggregation spatial attention

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Blind 360-degree image quality assessment (360-IQA) is crucial for immersive experiences.
Existing models often struggle with spatial significance and computational efficiency.

Purpose of the Study:

To develop an attention-aware patch-based deep-learning model for superior 360-IQA.
To improve accuracy, generalization, and computational efficiency compared to state-of-the-art methods.

Main Methods:

Utilized spatial attention mechanisms and skip connections for feature alignment.
Implemented adaptive patch sampling considering user exploration and latitude.
Employed an adaptive strategy for pooling local patch qualities, including outlier rejection and saliency weighting.

Main Results:

The proposed model significantly outperforms existing deep-learning, multichannel, and natural scene statistic-based models in accuracy and generalization.
Achieved substantial reduction in computational complexity compared to multichannel models.
Ablation studies confirmed the efficacy of individual model components.

Conclusions:

The attention-aware patch-based model represents a significant advancement in 360-IQA.
Offers a more accurate, generalizable, and computationally efficient solution for assessing 360-degree image quality.