检索结果-内蒙古大学图书馆

IS and T International Symposium on Electronic Imaging: 19th Image Quality and System Performance, IQSP 2022

作者： Sendjasni, Abderrezzaq Larabi, Mohamed-Chaker Cheikh, Faouzi Alaya CNRS Xlim UMR 7252 Université de Poitiers France NTNU Norwegian Colour and Visual Computing Lab Gjøvik Norway

Patch-based training for 360-degree images allows to significantly reduce the complexity compared to multichannel models while maintaining good performances. Differently from multichannel models where multi neural networks are trained in parallel to predict the score of the whole 360-degree image, a pooling stage is required to map local qualities to the global one. This step is often neglected by using a simple arithmetic mean, which does not account for (i) the non-uniformity distribution of quality and (ii) the variability among local qualities. In this paper, we analyze several pooling strategies, including basic statistic methods and adaptive pooling ones. Additionally, we propose a pooling strategy based on scene exploration behavior relying on visual scan-path. The performance analysis showed the benefit of using adaptive pooling over arithmetic mean, as well as the incorporation of perceptual properties during the pooling stage. Besides, the comparison with state-of-the-art multichannel models asserts the effectiveness of patch-based training compared to multichannel models. © 2022, Society for Imaging Science and Technology.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Contrast Enhancement: Cross-modal Learning Approach for Medical Images 20

Contrast Enhancement: Cross-modal Learning Approach for Medi...

引用

IS and T International Symposium on Electronic Imaging: 20th Image Processing: Algorithms and Systems, IPAS 2022

作者： Naseem, Rabia Islam, Akib Jayed Cheikh, Faouzi Alaya Beghdadi, Azeddine Norwegian Colour and Visual Computing Lab. Norwegian University of Science & Technology Norway University Jean Monnet Saint-Etienne France L2TI Institut Galilée Université Sorbonne Paris Nord France

Contrast is an imperative perceptible attribute embodying the image quality. In medical images, the poor quality, specifically low contrast inhibits precise interpretation of the image. Contrast enhancement is, therefore, applied not merely to improve the visual quality of images but also enabling them to facilitate further processing tasks. In this paper, we propose a contrast enhancement approach based on cross-modal learning using two-way Generative Adversarial Network (GAN), where U-Net augmented with global features acts as a generator. Besides, individual batch normalization has been used to make generators adapt specifically to their input distributions. The proposed method learns the global contrast characteristics of T1-w brain magnetic resonance images (MRI) to improve the contrast of T2-w images. The experiments were conducted on a publicly available IXI dataset. Comparison with recent CE methods and quantitative assessment using two prevalent metrics FSIM and BRISQUE validate the superior performance of the proposed method. © 2022, Society for Imaging Science and Technology.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

On the Influence of Head-Mounted Displays on Quality Rating of Omnidirectional Images 18

On the Influence of Head-Mounted Displays on Quality Rating ...

引用

18th Image Quality and System Performance Conference, IQSP 2021

作者： Sendjasni, Abderrezzaq Larabi, Mohamed-Chaker Cheikh, Faouzi Alaya CNRS Xlim UMR 7252 Université de Poitiers France NTNU Norwegian Colour and Visual Computing Lab Gjøvik Norway

In this paper, we propose to study the influence of using head-mounted displays (HMDs) for visual quality rating of omnidirectional images and their impact on the final quality scores. Because of the used display technology, these devices introduce a significant impairment called screen door effect that may alter the quality of experience. Furthermore, the extended use of such a technology may produce cyber-sickness. In this study, a subjective experiment is designed and carried out using various HMDs with various types of content. The statistically analysed results revealed a significant difference between HMDs for quality rating tasks on the overall ratings as well as per individual distortions. These findings will contribute to the development of a reliable protocol for omnidirectional subjective quality assessment, and the constructed database will be used as a ground-truth for quality metrics development. © 2021, Society for Imaging Science and Technology.

关键词： Helmet mounted displays

来源：评论

学校读者我要写书评

暂无评论

visual Scan-Path based Data-Augmentation for CNN-based 360-degree Image Quality Assessment

Visual Scan-Path based Data-Augmentation for CNN-based 360-d...

引用

London Imaging Meeting, LIM 2021

作者： Sendjasni, Abderrezzaq Larabi, Mohamed-Chaker Cheikh, Faouzi Alaya CNRS Xlim UMR 7252 Université de Poitiers France NTNU Norwegian Colour and Visual Computing Lab Gjøvik Norway

ISBN: (纸本)9780892083558

360-degree Image quality assessment (IQA) is facing the major challenge of lack of ground-truth databases. This prob lem is accentuated for deep learning based approaches where the performances are as good as the available data. In this context, only two databases are used to train and validate deep learning-based IQA models. To compensate this lack, a data augmentation technique is investigated in this paper. We use vi sual scan-path to increase the learning examples from existing training data. Multiple scan-paths are predicted to account for the diversity of human observers. These scan-paths are then used to select viewports from the spherical representation. The results of the data-augmentation training scheme showed an improve ment over not using it. We also try to answer the question of using the MOS obtained for the 360-degree image as the quality anchor for the whole set of extracted viewports in comparison to 2Dblind quality metrics. The comparison showed the superiority of using the MOS when adopting a patch-based learning. © 2021 Society for Imaging Science and Technology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multi-Criteria Contrast Enhancement Evaluation Measure using Wavelet Decomposition 22

A Multi-Criteria Contrast Enhancement Evaluation Measure usi...

引用

22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020

作者： Khan, Zohaib Amjad Beghdadi, Azeddine Cheikh, Faouzi Alaya Kaaniche, Mounir Ali Qureshi, Muhammad Université Sorbonne Paris Nord L2TI Villetaneuse France NTNU Norwegian Colour and Visual Computing Lab Gjovik Norway Islamia University of Bahawalpur 63100 Pakistan

ISBN: (纸本)9781728193205

An effective contrast enhancement method should not only improve the perceptual quality of an image but should also avoid adding any artifacts or affecting naturalness of images. This makes Contrast Enhancement Evaluation (CEE) a challenging task in the sense that both the improvement in image quality and unwanted side-effects need to be checked for. Currently, there is no single CEE metric that works well for all kinds of enhancement criteria. In this paper, we propose a new Multi-Criteria CEE (MCCEE) measure which combines different metrics effectively to give a single quality score. In order to fully exploit the potential of these metrics, we have further proposed to apply them on the decomposed image using wavelet transform. This new metric has been tested on two natural image contrast enhancement databases as well as on medical Computed Tomography (CT) images. The results show a substantial improvement as compared to the existing evaluation metrics. The code for the metric is available at: https://***/zakopz/MCCEE-Contrast-Enhancement-Metric © 2020 IEEE.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment

arXiv

引用

arXiv 2021年

作者： Khan, Zohaib Amjad Beghdadi, Azeddine Kaaniche, Mounir Cheikh, Faouzi Alaya Université Sorbonne Paris Nord L2TI UR 3043 VilletaneuseF-93430 France Norwegian Colour and Visual Computing Lab Gjøvik Norway

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Video Quality Assessment Dataset for Smart Public Security Systems 23

Video Quality Assessment Dataset for Smart Public Security S...

引用

23rd IEEE International Multi-Topic Conference, INMIC 2020

作者： Bezzine, Ismail Khan, Zohaib Amjad Beghdadi, Azeddine Al-Maadeed, Noor Kaaniche, Mounir Al-Maadeed, Somaya Bouridane, Ahmed Cheikh, Faouzi Alaya L2TI-Institut Galilée Universite Sorbonne Paris Nord Villetaneuse France Qatar University Doha Qatar Northumbria University Newcastle Upon Tyne United Kingdom Norwegian Colour and Visual Computing Lab Ntnu Gjovik Norway

ISBN: (纸本)9781728198934

Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems. Database link: https://***/datasets/prf8p9rwhd/draft?a=d9646d2f-5154-4d49-8eb3-e7989187a24c © 2020 IEEE.

关键词： Security systems

来源：评论

学校读者我要写书评

暂无评论

Adaptive Context Encoding Module for Semantic Segmentation 18

Adaptive Context Encoding Module for Semantic Segmentation

引用

18th Image Processing: Algorithms and Systems Conference, IPAS 2020

作者： Wang, Congcong Cheikh, Faouzi Alaya Beghdadi, Azeddine Elle, Ole Jakob Norwegian Colour and Visual Computing Lab Norwegian University of Science and Technology Norway L2TI-Institut Galilée Université Paris 13 Sorbonne Paris Cité Villetaneuse France Intervention Centre Oslo University Hospital Oslo Norway Department of Informatics University of Oslo Oslo Norway

The object sizes in images are diverse, therefore, capturing multiple scale context information is essential for semantic segmentation. Existing context aggregation methods such as pyramid pooling module (PPM) and atrous spatial pyramid pooling (ASPP) employ different pooling size or atrous rate, such that multiple scale information is captured. However, the pooling sizes and atrous rates are chosen empirically. Rethinking of ASPP leads to our observation that learnable sampling locations of the convolution operation can endow the network learnable field-of-view, thus the ability of capturing object context information adaptively. Following this observation, in this paper, we propose an adaptive context encoding (ACE) module based on deformable convolution operation where sampling locations of the convolution operation are learnable. Our ACE module can be embedded into other Convolutional Neural Networks (CNNs) easily for context aggregation. The effectiveness of the proposed module is demonstrated on Pascal-Context and ADE20K datasets. Although our proposed ACE only consists of three deformable convolution blocks, it outperforms PPM and ASPP in terms of mean Intersection of Union (mIoU) on both datasets. All the experimental studies confirm that our proposed module is effective compared to the state-of-the-art methods. © 2020, Society for Imaging Science and Technology

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Residual Networks Based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment

Residual Networks Based Distortion Classification and Rankin...

引用

IEEE International Conference on Image Processing

作者： Zohaib Amjad Khan Azeddine Beghdadi Mounir Kaaniche Faouzi Alaya Cheikh Université Sorbonne Paris Nord L2TI UR 3043 Villetaneuse France Norwegian University of Science and Technology (NTNU) Norwegian Colour and Visual Computing Lab Gjøvik Norway

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

关键词： Distortion Laparoscopes Task analysis Image quality Videos Neural networks Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Multi-Criteria Contrast Enhancement Evaluation Measure using Wavelet Decomposition

A Multi-Criteria Contrast Enhancement Evaluation Measure usi...

引用

IEEE Workshop on Multimedia Signal Processing

作者： Zohaib Amjad Khan Azeddine Beghdadi Faouzi Alaya Cheikh Mounir Kaaniche Muhammad Ali Qureshi L2TI Université Sorbonne Paris Nord Villetaneuse France Norwegian Colour and Visual Computing Lab NTNU Gjøvik Norway The Islamia University of Bahawalpur Pakistan

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

关键词： Measurement Image quality Deep learning Wavelet transforms Databases Computed tomography Task analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：