Attention-Based Color Difference Perception for Photographic Images
Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is...
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MDPI AG
2025-03-01
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| author | Hua Qiang Xuande Zhang Jinliang Hou |
| author_facet | Hua Qiang Xuande Zhang Jinliang Hou |
| author_sort | Hua Qiang |
| collection | DOAJ |
| description | Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is to propose a high-precision image CD measurement model that simulates the perceptual process of the human visual system and apply it to the CD perception of smartphone photography images. Based on this, a CD measurement network called CD-Attention is proposed, which integrates CNN and Vision Transformer features. First, a CNN and the ViT are used separately to extract local features and global semantic features from the reference image and the distorted image. Secondly, deformable convolution is used for attention guidance, utilizing the global semantic features of the ViT to direct CNN to focus on salient regions of the image, enhancing the transformation modeling capability of CNN features. Thirdly, through the feature fusion module, the CNN features that have been guided by attention are fused with the global semantic features of the ViT. Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. Validated on the large-scale SPCD dataset, the CD-Attention model has achieved state-of-the-art performance, outperforming 30 existing CD measurement methods and demonstrating useful generalization ability. It has been demonstrated that CD-Attention can achieve CD measurement for large-sized and content-complex smartphone photography images. At the same time, the effectiveness of CD-Attention’s feature extraction and attention guidance are verified by ablation experiments. |
| format | Article |
| id | doaj-art-bc12c72bcaa14de8a8c9e49de27c1df1 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-bc12c72bcaa14de8a8c9e49de27c1df12025-08-20T02:52:35ZengMDPI AGApplied Sciences2076-34172025-03-01155270410.3390/app15052704Attention-Based Color Difference Perception for Photographic ImagesHua Qiang0Xuande Zhang1Jinliang Hou2School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electronic Information and Artificial Intelligence, Xi’an Technological University, Xi’an 710021, ChinaTraditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is to propose a high-precision image CD measurement model that simulates the perceptual process of the human visual system and apply it to the CD perception of smartphone photography images. Based on this, a CD measurement network called CD-Attention is proposed, which integrates CNN and Vision Transformer features. First, a CNN and the ViT are used separately to extract local features and global semantic features from the reference image and the distorted image. Secondly, deformable convolution is used for attention guidance, utilizing the global semantic features of the ViT to direct CNN to focus on salient regions of the image, enhancing the transformation modeling capability of CNN features. Thirdly, through the feature fusion module, the CNN features that have been guided by attention are fused with the global semantic features of the ViT. Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. Validated on the large-scale SPCD dataset, the CD-Attention model has achieved state-of-the-art performance, outperforming 30 existing CD measurement methods and demonstrating useful generalization ability. It has been demonstrated that CD-Attention can achieve CD measurement for large-sized and content-complex smartphone photography images. At the same time, the effectiveness of CD-Attention’s feature extraction and attention guidance are verified by ablation experiments.https://www.mdpi.com/2076-3417/15/5/2704image quality assessmentcolor difference perceptionattention mechanismvision transformerdeformable convolution |
| spellingShingle | Hua Qiang Xuande Zhang Jinliang Hou Attention-Based Color Difference Perception for Photographic Images Applied Sciences image quality assessment color difference perception attention mechanism vision transformer deformable convolution |
| title | Attention-Based Color Difference Perception for Photographic Images |
| title_full | Attention-Based Color Difference Perception for Photographic Images |
| title_fullStr | Attention-Based Color Difference Perception for Photographic Images |
| title_full_unstemmed | Attention-Based Color Difference Perception for Photographic Images |
| title_short | Attention-Based Color Difference Perception for Photographic Images |
| title_sort | attention based color difference perception for photographic images |
| topic | image quality assessment color difference perception attention mechanism vision transformer deformable convolution |
| url | https://www.mdpi.com/2076-3417/15/5/2704 |
| work_keys_str_mv | AT huaqiang attentionbasedcolordifferenceperceptionforphotographicimages AT xuandezhang attentionbasedcolordifferenceperceptionforphotographicimages AT jinlianghou attentionbasedcolordifferenceperceptionforphotographicimages |