Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution

Yuan, Feng; Shao, Xiao Dong

doi:10.32604/jbd.2020.015357

Cited by 5 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to refs. [1–39], we use two widely used metrics to measure the prediction accuracy of IQA models. One is the Pearson linear correlation coefficient (PLCC).…”

Section: Methodsmentioning

confidence: 99%

“…Hence, it is urgent to develop an effective computer algorithm to automatically predict image quality scores, which is known as the objective IQA method. According to the use of reference information, the objective IQA can be divided into three categories: full‐reference IQA (FR‐IQA) [1–5], reduce‐rReference IQA (RR‐IQA) [6], and no‐reference IQA (NR‐IQA) [7–10]. With full or partial reference information for comparison, these FR‐IQA and RR‐IQA methods have achieved great prediction accuracy over the past decades.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it is urgent to develop an effective computer algorithm to automatically predict image quality scores, which is known as the objective IQA method. According to the use of reference information, the objective IQA can be divided into three categories: full-reference IQA (FR-IQA) [1][2][3][4][5], reduce-rReference IQA (RR-IQA) [6], and no-reference IQA (NR-IQA) [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CPDINet: Blind image quality assessment via a content perception and distortion inference network

Shao

Liu

Zhang

2022

IET Image Processing

Self Cite

View full text Add to dashboard Cite

Nowadays, it is still challenging for the blind image quality assessment (BIQA) to accurately predict quality scores of distorted images, since distorted images have rich content information and complex distortions. To solve these problems, a content perception and distortion inference network for BIQA is proposed, which divides IQA task into content perception and distortion inference processes. Since humans try to understand image content before perceiving quality scores, a content feature extractor is designed to explore content information in an image to deal with the content variation problem. To handle the distortion diversity problem, a distortion feature extractor is proposed to capture distortion features in images. Because extracted content features and distortion ones have different characteristics, attention-based fusion blocks to fuse multi-scale content features and distortion ones as guidance to selectively enhance important features based on calculated weight scores are proposed. With fused features, a quality prediction module is designed to regress multi-scale features to quality scores. Experiments are performed on six public IQA datasets, including LIVE, CSIQ, TID2013, LIVEC, KonIQ-10k, and SPAQ. Experimental results show that the proposed method can effectively predict quality scores for both synthetically and authentically distorted images than its peers, including the state-of-the-art methods.

show abstract

“…Similar to refs. [1–39], we use two widely used metrics to measure the prediction accuracy of IQA models. One is the Pearson linear correlation coefficient (PLCC).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Hence, it is urgent to develop an effective computer algorithm to automatically predict image quality scores, which is known as the objective IQA method. According to the use of reference information, the objective IQA can be divided into three categories: full-reference IQA (FR-IQA) [1][2][3][4][5], reduce-rReference IQA (RR-IQA) [6], and no-reference IQA (NR-IQA) [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CPDINet: Blind image quality assessment via a content perception and distortion inference network

Shao

Liu

Zhang

2022

IET Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Secondly, it is usually difficult to propose a novel network architecture that could be powerful on both synthetic and authentic distortions. For the first obstacle, to avoid the overfitting problem induced by the insufficient IQA databases, many CNNbased methods [17][18][19][20] crop large images into sub-images for data augmentation. Besides, the random horizontal flip method and transfer learning method are further employed in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many studies have shown that convolutional neural networks (CNN) significantly improve the performance of various vision tasks [14][15][16]. And many researchers focus on the exploration of the CNN-based NR-IQA methods [17][18][19]. Nevertheless, in IQA databases, since synthetic distortions such as JPEG, Impulse noise, Contrast change, and Gaussian blur are simulated in the laboratory, and the authentic distortions are captured in the wild, the distortion characteristics of the two are quite different.…”

Section: Introductionmentioning

confidence: 99%

No‐reference image quality assessment based on multiscale feature representation

Qiao

Zhao

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

The no-reference image quality assessment (NR-IQA) method can evaluate the distortions in an image without the reference image. However, due to the diversity of the image contents and distortion types, it is hard for the existing NR-IQA algorithms to obtain competitive performance on both synthetically and authentically distorted images. To address the problem, a multiscale feature representation-based NR-IQA method that performs well for both synthetic and authentic distortions is proposed. This model consists of two parts: The feature extraction part and the feature fusion part. First, part of the Res2Net-50 network is chosen as the feature extraction part due to its high ability in increasing the range of receptive fields. Then, the feature fusion part consisting of a novel residual block and two fully connected layers is designed to fuse the extracted features and realize the quality score mapping. After a series of stepwise optimization experiments, the most competitive network architecture consisting of the feature extraction part and the feature fusion part is obtained. Comprehensive experiments on the LIVE, TID2013, CSIQ, KADID-10k, KonIQ-10K, and LIVE challenge databases demonstrate that the proposed method can work powerfully on both the synthetic and authentic distortions and also has a strong generalization ability.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Multi-sequence Satellite Image Cloud Removal Based on Dual Residual Network

Xiao

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution

Cited by 5 publications

References 0 publications

CPDINet: Blind image quality assessment via a content perception and distortion inference network

CPDINet: Blind image quality assessment via a content perception and distortion inference network

No‐reference image quality assessment based on multiscale feature representation

Multi-sequence Satellite Image Cloud Removal Based on Dual Residual Network

Contact Info

Product

Resources

About