A lightweight approach for image quality assessment

Image quality assessment is a vital computer vision task for image validation and visual experience development. Lately, most research in this field has focused on enhancing model performance, resulting in a significant growth in model size. Those models may require considerable storage resources an...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2024-09, Vol.18 (10), p.6761-6768
Main Authors: Dang, Quang Minh, Truong, Minh Tuyen, Dang, Tuan Linh
Format: Article
Language:English
Subjects:
Computer Imaging
Computer Science
Computer vision
Image enhancement
Image Processing and Computer Vision
Image quality
Lightweight
Modules
Multimedia Information Systems
Original Paper
Parameters
Pattern Recognition and Graphics
Quality assessment
Signal,Image and Speech Processing
Vision
Visual fields
Visual tasks
Weight reduction
Citations: Items that this one cites
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Summary:Image quality assessment is a vital computer vision task for image validation and visual experience development. Lately, most research in this field has focused on enhancing model performance, resulting in a significant growth in model size. Those models may require considerable storage resources and computational costs. Additionally, they have yet to focus on the small-parameter models. Therefore, to contribute a lightweight model for this topic, this paper proposes a new module called GhostDPD and uses it to construct a MobileDPD model. The GhostDPD structure has lightweight attention layers, two depth-wise convolutional layers, and a module with fewer parameters to replace the point-wise layer. Experiments with the different datasets showed that the proposed model achieved similar results to state-of-the-art approaches despite being much smaller.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-024-03349-0