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

Sensors (Basel, Switzerland)·2023
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PW-360IQA: Perceptually-Weighted Multichannel 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)
|May 13, 2023
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Summary

This study introduces a new machine learning model for assessing 360-degree image quality. The perceptually weighted multichannel convolutional neural network (CNN) improves accuracy while reducing computational complexity.

Keywords:
360-degree imagesCNNsJNDblind image quality assessmentscan-path

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Assessing 360-degree image quality using machine learning presents significant challenges.
  • Existing methods often lack robust training strategies and optimized model architectures.

Purpose of the Study:

  • To propose a novel perceptually weighted multichannel convolutional neural network (CNN) for 360-degree image quality assessment (IQA).
  • To enhance IQA by incorporating user exploration behavior and human visual system (HVS) properties.

Main Methods:

  • Developed a perceptually weighted multichannel CNN (PW-360IQA) utilizing a weight-sharing strategy.
  • Extracted visually important viewports based on scan-path predictions and integrated visual trajectory data.
  • Employed DenseNet-121 as the backbone and incorporated distortion probability maps and inter-observer variability.

Main Results:

  • The PW-360IQA model demonstrated robust performance on CVIQ and OIQA datasets.
  • The approach achieved comparable or superior results to state-of-the-art methods.
  • Significantly reduced computational complexity compared to existing solutions.

Conclusions:

  • The proposed PW-360IQA model offers an effective and efficient solution for 360-degree image quality assessment.
  • Integrating perceptual weighting, user behavior, and HVS properties enhances IQA accuracy.
  • The model's reduced complexity makes it a practical advancement in the field.