GSBIQA: Green Saliency-guided Blind Image Quality Assessment Method
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916315759902720 |
|---|---|
| author | Mei, Zhanxuan Wang, Yun-Cheng Kuo, C. -C. Jay |
| author_facet | Mei, Zhanxuan Wang, Yun-Cheng Kuo, C. -C. Jay |
| contents | Blind Image Quality Assessment (BIQA) is an essential task that estimates the perceptual quality of images without reference. While many BIQA methods employ deep neural networks (DNNs) and incorporate saliency detectors to enhance performance, their large model sizes limit deployment on resource-constrained devices. To address this challenge, we introduce a novel and non-deep-learning BIQA method with a lightweight saliency detection module, called Green Saliency-guided Blind Image Quality Assessment (GSBIQA). It is characterized by its minimal model size, reduced computational demands, and robust performance. Experimental results show that the performance of GSBIQA is comparable with state-of-the-art DL-based methods with significantly lower resource requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GSBIQA: Green Saliency-guided Blind Image Quality Assessment Method Mei, Zhanxuan Wang, Yun-Cheng Kuo, C. -C. Jay Image and Video Processing Blind Image Quality Assessment (BIQA) is an essential task that estimates the perceptual quality of images without reference. While many BIQA methods employ deep neural networks (DNNs) and incorporate saliency detectors to enhance performance, their large model sizes limit deployment on resource-constrained devices. To address this challenge, we introduce a novel and non-deep-learning BIQA method with a lightweight saliency detection module, called Green Saliency-guided Blind Image Quality Assessment (GSBIQA). It is characterized by its minimal model size, reduced computational demands, and robust performance. Experimental results show that the performance of GSBIQA is comparable with state-of-the-art DL-based methods with significantly lower resource requirements. |
| title | GSBIQA: Green Saliency-guided Blind Image Quality Assessment Method |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2407.05590 |