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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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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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Related Experiment Video

Updated: Jul 11, 2025

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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
PubMed
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 imagesadaptive samplingconvolutional neural networksimage quality assessmentsaliency-based aggregationspatial 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.