A lightweight handcrafted feature-based selective attention network for blind image quality assessment
This paper introduces a novel approach to Blind Image Quality Assessment (BIQA) by employing handcrafted features combined with a selective feature attention mechanism, drawing inspiration from the human visual system (HVS). This method aligns more closely with human perception of image quality, as...
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2023
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sg-ntu-dr.10356-1660462023-04-21T15:39:20Z A lightweight handcrafted feature-based selective attention network for blind image quality assessment Feng, HaoLin Lin Weisi School of Computer Science and Engineering WSLin@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision This paper introduces a novel approach to Blind Image Quality Assessment (BIQA) by employing handcrafted features combined with a selective feature attention mechanism, drawing inspiration from the human visual system (HVS). This method aligns more closely with human perception of image quality, as it integrates knowledge from the HVS. The handcrafted features utilized in this method include the Laplace operator, Scharr filter, and Discrete Cosine Transform (DCT), which were selected for their ability to capture essential low-level image properties critical for subjective image quality assessment tasks. The Laplace operator serves as an edge detection tool, the Scharr filter is a derivative-based filter for identifying edges in images, and the DCT helps analyze the frequency content of images. Compared to conventional BIQA algorithms, this method demonstrates improved accuracy while maintaining low memory complexity, making it suitable for real-time applications in image processing. This is particularly valuable in resource-constrained contexts where memory utilization is a major concern. The efficacy of this approach was validated through experiments on four synthetic distortion IQA datasets, with results indicating that the proposed method surpasses traditional BIQA algorithms in performance. In summary, the innovative BIQA method incorporates handcrafted features and a selective feature attention mechanism, drawing from the Human Visual System (HVS) to enhance image quality evaluation accuracy while preserving low memory complexity. Bachelor of Engineering (Computer Science) 2023-04-19T06:08:49Z 2023-04-19T06:08:49Z 2023 Final Year Project (FYP) Feng, H. (2023). A lightweight handcrafted feature-based selective attention network for blind image quality assessment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166046 https://hdl.handle.net/10356/166046 en PSCSE21-0015 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Feng, HaoLin A lightweight handcrafted feature-based selective attention network for blind image quality assessment |
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This paper introduces a novel approach to Blind Image Quality Assessment (BIQA) by employing handcrafted features combined with a selective feature attention mechanism, drawing inspiration from the human visual system (HVS). This method aligns more closely with human perception of image quality, as it integrates knowledge from the HVS.
The handcrafted features utilized in this method include the Laplace operator, Scharr filter, and Discrete Cosine Transform (DCT), which were selected for their ability to capture essential low-level image properties critical for subjective image quality assessment tasks. The Laplace operator serves as an edge detection tool, the Scharr filter is a derivative-based filter for identifying edges in images, and the DCT helps analyze the frequency content of images.
Compared to conventional BIQA algorithms, this method demonstrates improved accuracy while maintaining low memory complexity, making it suitable for real-time applications in image processing. This is particularly valuable in resource-constrained contexts where memory utilization is a major concern.
The efficacy of this approach was validated through experiments on four synthetic distortion IQA datasets, with results indicating that the proposed method surpasses traditional BIQA algorithms in performance. In summary, the innovative BIQA method incorporates handcrafted features and a selective feature attention mechanism, drawing from the Human Visual System (HVS) to enhance image quality evaluation accuracy while preserving low memory complexity. |
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Lin Weisi |
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Lin Weisi Feng, HaoLin |
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Final Year Project |
author |
Feng, HaoLin |
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Feng, HaoLin |
title |
A lightweight handcrafted feature-based selective attention network for blind image quality assessment |
title_short |
A lightweight handcrafted feature-based selective attention network for blind image quality assessment |
title_full |
A lightweight handcrafted feature-based selective attention network for blind image quality assessment |
title_fullStr |
A lightweight handcrafted feature-based selective attention network for blind image quality assessment |
title_full_unstemmed |
A lightweight handcrafted feature-based selective attention network for blind image quality assessment |
title_sort |
lightweight handcrafted feature-based selective attention network for blind image quality assessment |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/166046 |
_version_ |
1764208156832956416 |