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Saliency-based deep convolutional neural network for no-reference image quality assessment

Sen Jia, Yang Zhang
2017 Multimedia tools and applications  
In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map.  ...  A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image.  ...  This paper aims at solving all the three points by combining Convolutional Neural Networks (CNNs) with saliency map.  ... 
doi:10.1007/s11042-017-5070-6 fatcat:x3x5f6imcbhzpba6amgql3dhbm

Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency [article]

Wei Lu, Wei Sun, Wenhan Zhu, Xiongkuo Min, Zicheng Zhang, Tao Wang, Guangtao Zhai
2022 arXiv   pre-print
deep neural network for the blind quality assessment of the SIs, which helps IVSS to filter the low-quality SIs and improve the detection and recognition performance.  ...  fully connected (FC) network respectively.  ...  the SI quality database (SIQD) [2] , and propose a visual saliency based deep neural network for the blind quality assessment of the SIs.  ... 
arXiv:2206.04318v1 fatcat:k3ankraqmnf4deuktsj2nt3ogm

Multi Information Fusion Network for Saliency Quality Assessment

Kai TAN, Qingbo WU, Fanman MENG, Linfeng XU
2019 IEICE transactions on information and systems  
Experimental results verify the effectiveness of our method. key words: saliency quality assessment, multi information, deep convolutional neural network, image content  ...  Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth.  ...  Introduction Saliency quality assessment is a kind of non-reference image quality evaluation method [1] , which estimates the quality of a saliency map without ground-truth.  ... 
doi:10.1587/transinf.2019edl8002 fatcat:axlnf3kevnb23ksbb4wfvke7du

Related Work on Image Quality Assessment [article]

Dongxu Wang
2022 arXiv   pre-print
This article will review the state-of-the-art image quality assessment algorithms.  ...  Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based  ...  In order to predict the quality score of saliency map by only looking over the saliency map itself. L Tang et al. [44] proposed deep saliency quality assessment network (DSQAN).  ... 
arXiv:2111.06291v2 fatcat:bcmfvfz2x5e4jitgzxj5t3fqyy

Depth Quality Aware Salient Object Detection [article]

Chenglizhao Chen, Jipeng Wei, Chong Peng, Hong Qin
2020 arXiv   pre-print
Thus, this paper attempts to integrate a novel depth quality aware subnet into the classic bi-stream structure, aiming to assess the depth quality before conducting the selective RGB-D fusion.  ...  between RGB and D, leading to poor fusion results in facing of low-quality D.  ...  Also, it is worthy mentioning that the pre-trained RGB saliency deep models can be directly applied as the RGB saliency subnet in the bi-stream network structure. B.  ... 
arXiv:2008.04159v1 fatcat:t2o6s7xalrgy3lkvengsggw6va

Image Aesthetics Assessment using Multi Channel Convolutional Neural Networks [article]

Nishi Doshi, Gitam Shikhenawis, Suman K Mitra
2019 arXiv   pre-print
In this article, the focus is on categorizing the images in high quality and low quality image. Deep convolutional neural networks are used to classify the images.  ...  Image Aesthetics Assessment is one of the emerging domains in research.  ...  Deep learning for IAA In this paper, deep convolutional neural networks are used for assessing the aesthetic quality of the images.  ... 
arXiv:1911.09301v1 fatcat:rcgvpfnokvf7jdzc34qty5m5la

Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images

Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu
2020 2020 IEEE International Conference on Image Processing (ICIP)  
Saliency has been widely studied in relation to image quality assessment (IQA).  ...  In this paper, we analyse the ability of deep learning versus traditional algorithms in predicting saliency, based on an IQA-aware saliency benchmark, the SIQ288 database.  ...  Knowing where people look in images helps understand how humans assess image quality [2] .  ... 
doi:10.1109/icip40778.2020.9191203 dblp:conf/icip/ZhaoLGSL20 fatcat:yteoqerkvfayrpmfvsmzzgadka

A Novel Just-Noticeable-Difference-based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions [article]

Soomin Seo, Sehwan Ki, Munchurl Kim
2020 arXiv   pre-print
Recently, due to the strength of deep convolutional neural networks (CNN), many CNN-based image quality assessment (IQA) models have been studied.  ...  However, in this paper, we propose a novel saliency-channel attention residual network based on the just-noticeable-difference (JND) concept for full-reference image quality assessments (FR-IQA).  ...  Inspired by these observations, our deep-learning-based full-reference image quality assessment (FR-IQA) network is designed by incorporating certain perception characteristics of A Novel Just-Noticeable-Difference-based  ... 
arXiv:1902.05316v4 fatcat:c3tdf6nmrnc7jljohtzw2gkd6u

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
Saliency Prediction via Orientation Selectivity Xia, Zhifang No-Reference Objective Quality Assessment Method of Display Products Xiang, Guoqing A Novel Quality Enhanced Low Complexity Ra Control  ...  Li, Lin Deep Blind Video Quality Assessment for User Generated Videos Li, MingKun Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Perso Re-Identification  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection [article]

Xuehao Wang, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
2020 arXiv   pre-print
To be more concrete, we conduct D quality assessments for each image region, following a multi-scale methodology that includes low-level edge consistency, mid-level regional uncertainty and high-level  ...  Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage  ...  [26] propose a depth-enhanced network, which consists of two subnetworks; i.e., one master network aims for RGB saliency computation, and the other makes full use of D saliency by integrating its deep  ... 
arXiv:2008.04157v1 fatcat:ypoajgewwzd7fci3hljjpylgzu

Table of Contents

2020 IEEE transactions on multimedia  
Tekalp 1005 Deep Learning for Multimedia Analysis Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Ghosh 912 Multimedia Using Haptic and Physiological Information Vibrotactile Quality Assessment: Hybrid Metric Design Based on SNR and SSIM . . . . . . X. Liu, M. Dohler, and Y.  ... 
doi:10.1109/tmm.2020.2980723 fatcat:6u6zz25ltvhmheqdoixyu2utwu

DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision

Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox
2019 Neural Information Processing Systems  
Deep neural network (DNN) based salient object detection in images based on highquality labels is expensive.  ...  Each handcrafted method is substituted by a deep network that learns to generate the pseudo-labels.  ...  We analyze the quality of the generated saliency maps (pseudo labels) from the deep networks and also the quality of aggregated MVA maps.  ... 
dblp:conf/nips/NguyenDMNNLB19 fatcat:atmmzgnwqjeklprp3tqzaqzbcu

Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images

Yuqi Dai, Changbin Xue, Li Zhou, Zhaoqing Pan
2022 PLoS ONE  
A modified model based on the residual network is exploited to extract the saliency map for a given image automatically.  ...  Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation  ...  Image quality assessment is the quantification of human perception of image quality.  ... 
doi:10.1371/journal.pone.0263729 pmid:35139132 pmcid:PMC8827453 fatcat:af4mpsjcgfe6phifynqobsyyei

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

Sebastian Bosse, Dominique Maniry, Klaus-Robert Muller, Thomas Wiegand, Wojciech Samek
2018 IEEE Transactions on Image Processing  
We present a deep neural network-based approach to image quality assessment (IQA).  ...  and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework.  ...  DEEP NEURAL NETWORKS FOR IMAGE QUALITY ASSESSMENT A. Neural Network-Based FR IQA Siamese networks have been used to learn similarity relations between two inputs.  ... 
doi:10.1109/tip.2017.2760518 pmid:29028191 fatcat:csr3qjozbzca7hly7nlchhdv5u

A Plug-and-play Scheme to Adapt Image Saliency Deep Model for Video Data [article]

Yunxiao Li, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
2020 arXiv   pre-print
Moreover, our method is simple yet effective for adapting any off-the-shelf pre-trained image saliency deep model to obtain high-quality video saliency detection.  ...  Thus, the most recent video saliency detection approaches have adopted the network architecture starting with a spatial deep model that is followed by an elaborately designed temporal deep model.  ...  deep model for a high-quality temporal saliency estimation; 2) Based on the saliency detection results of the bi-stream network, we attempt to rapidly identify video frames with high quality video saliency  ... 
arXiv:2008.09103v1 fatcat:mz7wcpw26ja6tispl3c6mmgnqm
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