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Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment.

Lintao Han1,2, Hengyi Lv1, Yuchen Zhao1

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

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Summary

We developed Conv-Former, a novel network for no-reference image quality assessment (NR-IQA). This model accurately evaluates both authentic and synthetic image distortions, outperforming existing methods.

Keywords:
deep model interationimage quality assessmentneural networkself-attentionvision transformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • No-reference image quality assessment (NR-IQA) is crucial for evaluating image authenticity and distortion.
  • Existing NR-IQA methods struggle with both authentic and synthetic image distortions.
  • Accurate image quality assessment (IQA) models require robust perceptual mechanisms.

Purpose of the Study:

  • To propose a novel network, Conv-Former, for NR-IQA.
  • To enhance representation learning for improved image content understanding.
  • To achieve state-of-the-art performance on both authentic and synthetic image databases.

Main Methods:

  • Utilizing a multi-stage transformer architecture inspired by ResNet-50 for perceptual mechanisms.
  • Implementing adaptive learnable position embedding for arbitrary image resolutions.
  • Introducing a new transformer block (TB) combining long-range dependencies and local information perception (LIP).
  • Employing dual path pooling (DPP) to preserve contextual image quality information during feature downsampling.

Main Results:

  • Conv-Former outperforms state-of-the-art methods on authentic image databases.
  • Conv-Former achieves competitive performance on synthetic image databases.
  • Experimental results demonstrate strong fitting performance and generalization capability.

Conclusions:

  • Conv-Former offers a robust solution for NR-IQA across diverse image distortion types.
  • The proposed network effectively integrates convolutional and self-attention mechanisms for enhanced IQA.
  • Conv-Former shows significant potential for real-world image quality evaluation applications.