GreenBIQA: A Lightweight Blind Image Quality Assessment Method

Mei, Zhanxuan; Wang, Yuncheng; He, Xingze; Kuo, C.-C. Jay

doi:10.1109/mmsp55362.2022.9948927

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…It is worthwhile to point out that preliminary results of our research were presented in [13]. This work is its extension.…”

Section: Introductionmentioning

confidence: 75%

Lightweight High-Performance Blind Image Quality Assessment

Mei¹,

Wang²,

He³

et al. 2023

Preprint

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Blind image quality assessment (BIQA) is a task that predicts the perceptual quality of an image without its reference. Research on BIQA attracts growing attention due to the increasing amount of user-generated images and emerging mobile applications where reference images are unavailable. The problem is challenging due to the wide range of content and mixed distortion types. Many existing BIQA methods use deep neural networks (DNNs) to achieve high performance. However, their large model sizes hinder their applicability to edge or mobile devices. To meet the need, a novel BIQA method with a small model, low computational complexity, and high performance is proposed and named "GreenBIQA" in this work. GreenBIQA includes five steps: 1) image cropping, 2) unsupervised representation generation, 3) supervised feature selection, 4) distortionspecific prediction, and 5) regression and decision ensemble. Experimental results show that the performance of GreenBIQA is comparable with that of state-of-the-art deep-learning (DL) solutions while demanding a much smaller model size and significantly lower computational complexity.

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“…It is worthwhile to point out that preliminary results of our research were presented in [13]. This work is its extension.…”

Section: Introductionmentioning

confidence: 75%

Lightweight High-Performance Blind Image Quality Assessment

Mei¹,

Wang²,

He³

et al. 2023

Preprint

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show abstract

“…Tables 5 and 6 list the performance compared with the currently existing 15 NR-IQA methods, among which are the traditional NSS methods (NRSL, 6 NCMQA, 8 HOSA, 35 and FRIQUEE 36 ) and learning-based methods (DBCNN, 10 MetaIQA, 11 HyperIQA, 13 VCRNet, 14 MANIQA, 37 MEON, 37 WaDIQaM, 38 H-IQA, 42 TIQA, 40 AIGQA, 39 TReS, 43 GreenBIQA, 44 and CONTRIQUE 45 ). The specific comparison results are illustrated in Tables 5 and 6.…”

Section: Experimental and Analysismentioning

confidence: 99%

Dual-band color fusion image quality assessment based on color descriptors

Gao,

Song,

Zhang

et al. 2024

J. Electron. Imag.

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Significant progress has been made in no-reference image quality assessment in terms of color harmony in recent years. However, most methods do not consider color contrast and lose some color information in the quantification process, so they perform poorly when evaluating the color harmony of color images. An image quality assessment model based on color descriptors is established under the inspiration of natural scene statistics. The model introduces local color contrast features of color fusion images and combines them with visual attention mechanism. The local color statistics coefficients of images in both RGB color space and CIELAB color space are calculated to reduce the loss of color information. Furthermore, a multi-scale content-aware network is used to extract multi-scale content features and establish the connection between color features and image content. Finally, the image quality score is obtained by the quality assessment network. The dataset validation and experimental results reveal that the proposed model, compared with the existing 15 methods, has good agreement with human visual perception and better performance.

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“…image generation [16][17][18], blind image quality assessment [19], disease classification [20], face gender classification [21], and object tracking [22][23][24].…”

Section: (I)mentioning

confidence: 99%

Methodology of soft partition for image classification

Mishra,

Kuo

2024

Disruptive Technologies in Information Sciences VIII

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The idea of subspace learning machine (SLM) has been a powerful tool for machine learning (ML), and it has been successfully applied to the task of image classification. Recently, a novel SLM method was proposed, which (i) projects high-dimensional feature vectors into a 1D feature subspace, and (ii) partitions it into two disjoint sets. SLM with soft partitioning (SLM/SP) extends this approach by learning an adaptive soft decision tree (SDT) structure using local greedy subspace partitioning. After meeting the stopping criteria for all child nodes and determining the tree structure, it updates all projection vectors (PVs) globally. It enables efficient training, high classification accuracy, and a small model size. It is applied to experimental data to show its performance as a lightweight and high-performance classification method.

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GreenBIQA: A Lightweight Blind Image Quality Assessment Method

Cited by 9 publications

References 23 publications

Lightweight High-Performance Blind Image Quality Assessment

Lightweight High-Performance Blind Image Quality Assessment

Dual-band color fusion image quality assessment based on color descriptors

Methodology of soft partition for image classification

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