JP6544543B2 - 畳み込みニューラルネットワークに基づいたフルリファレンス画像品質評価方法 - Google Patents

畳み込みニューラルネットワークに基づいたフルリファレンス画像品質評価方法 Download PDF

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JP6544543B2
JP6544543B2 JP2017563173A JP2017563173A JP6544543B2 JP 6544543 B2 JP6544543 B2 JP 6544543B2 JP 2017563173 A JP2017563173 A JP 2017563173A JP 2017563173 A JP2017563173 A JP 2017563173A JP 6544543 B2 JP6544543 B2 JP 6544543B2
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image
reference image
distortion
similarity
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JP2018516412A (ja
JP2018516412A5 (enExample
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シュン シュー
シュン シュー
ペン イェ
ペン イェ
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Sony Corp
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    • G06T7/0004Industrial image inspection
    • G06T7/001Industrial image inspection using an image reference approach
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/241Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133Distances to prototypes
    • G06F18/24137Distances to cluster centroïds
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    • G06N3/0464Convolutional networks [CNN, ConvNet]
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    • G06V10/82Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • GPHYSICS
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    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/98Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G06V10/993Evaluation of the quality of the acquired pattern
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    • H04N17/004Diagnosis, testing or measuring for television systems or their details for digital television systems
    • HELECTRICITY
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    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/154Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
    • GPHYSICS
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    • G06T2207/00Indexing scheme for image analysis or image enhancement
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    • G06T2207/10004Still image; Photographic image
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    • G06T2207/10016Video; Image sequence
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  • Bioinformatics & Computational Biology (AREA)
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JP2017563173A 2015-06-05 2016-06-03 畳み込みニューラルネットワークに基づいたフルリファレンス画像品質評価方法 Active JP6544543B2 (ja)

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US14/732,518 2015-06-05
US14/732,518 US9741107B2 (en) 2015-06-05 2015-06-05 Full reference image quality assessment based on convolutional neural network
PCT/US2016/035868 WO2016197026A1 (en) 2015-06-05 2016-06-03 Full reference image quality assessment based on convolutional neural network

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JP6544543B2 true JP6544543B2 (ja) 2019-07-17

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EP (1) EP3292512B1 (enExample)
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KR (1) KR101967089B1 (enExample)
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WO (1) WO2016197026A1 (enExample)

Families Citing this family (102)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11263432B2 (en) * 2015-02-06 2022-03-01 Veridium Ip Limited Systems and methods for performing fingerprint based user authentication using imagery captured using mobile devices
US9424458B1 (en) 2015-02-06 2016-08-23 Hoyos Labs Ip Ltd. Systems and methods for performing fingerprint based user authentication using imagery captured using mobile devices
US9734567B2 (en) * 2015-06-24 2017-08-15 Samsung Electronics Co., Ltd. Label-free non-reference image quality assessment via deep neural network
US10410330B2 (en) * 2015-11-12 2019-09-10 University Of Virginia Patent Foundation System and method for comparison-based image quality assessment
US10356343B2 (en) * 2016-03-18 2019-07-16 Raytheon Company Methods and system for geometric distortion correction for space-based rolling-shutter framing sensors
US9904871B2 (en) * 2016-04-14 2018-02-27 Microsoft Technologies Licensing, LLC Deep convolutional neural network prediction of image professionalism
US10043254B2 (en) 2016-04-14 2018-08-07 Microsoft Technology Licensing, Llc Optimal image transformation based on professionalism score of subject
US10043240B2 (en) 2016-04-14 2018-08-07 Microsoft Technology Licensing, Llc Optimal cropping of digital image based on professionalism score of subject
WO2018033137A1 (zh) * 2016-08-19 2018-02-22 北京市商汤科技开发有限公司 在视频图像中展示业务对象的方法、装置和电子设备
US10360494B2 (en) * 2016-11-30 2019-07-23 Altumview Systems Inc. Convolutional neural network (CNN) system based on resolution-limited small-scale CNN modules
US10834406B2 (en) * 2016-12-12 2020-11-10 Netflix, Inc. Device-consistent techniques for predicting absolute perceptual video quality
US11113800B2 (en) 2017-01-18 2021-09-07 Nvidia Corporation Filtering image data using a neural network
US11537869B2 (en) * 2017-02-17 2022-12-27 Twitter, Inc. Difference metric for machine learning-based processing systems
WO2018152741A1 (en) * 2017-02-23 2018-08-30 Nokia Technologies Oy Collaborative activation for deep learning field
CN106920215B (zh) * 2017-03-06 2020-03-27 长沙全度影像科技有限公司 一种全景图像配准效果的检测方法
CN108304755B (zh) * 2017-03-08 2021-05-18 腾讯科技(深圳)有限公司 用于图像处理的神经网络模型的训练方法和装置
CN107103331B (zh) * 2017-04-01 2020-06-16 中北大学 一种基于深度学习的图像融合方法
WO2018186991A1 (en) * 2017-04-04 2018-10-11 Board Of Regents, The University Of Texas System Assessing quality of images or videos using a two-stage quality assessment
US10699160B2 (en) 2017-08-23 2020-06-30 Samsung Electronics Co., Ltd. Neural network method and apparatus
CN107644415B (zh) * 2017-09-08 2019-02-22 众安信息技术服务有限公司 一种文本图像质量评估方法及设备
CN107705299B (zh) * 2017-09-25 2021-05-14 安徽睿极智能科技有限公司 基于多属性特征的图像质量分类方法
CN107679490B (zh) * 2017-09-29 2019-06-28 百度在线网络技术(北京)有限公司 用于检测图像质量的方法和装置
US10540589B2 (en) * 2017-10-24 2020-01-21 Deep North, Inc. Image quality assessment using similar scenes as reference
CN108171256A (zh) * 2017-11-27 2018-06-15 深圳市深网视界科技有限公司 人脸图像质评模型构建、筛选、识别方法及设备和介质
US10740659B2 (en) * 2017-12-14 2020-08-11 International Business Machines Corporation Fusing sparse kernels to approximate a full kernel of a convolutional neural network
CN108074239B (zh) * 2017-12-30 2021-12-17 中国传媒大学 一种基于先验感知质量特征图的无参考图像质量客观评价方法
CN108335289A (zh) * 2018-01-18 2018-07-27 天津大学 一种全参考融合的图像客观质量评价方法
US10721477B2 (en) * 2018-02-07 2020-07-21 Netflix, Inc. Techniques for predicting perceptual video quality based on complementary perceptual quality models
US10887602B2 (en) 2018-02-07 2021-01-05 Netflix, Inc. Techniques for modeling temporal distortions when predicting perceptual video quality
CN108389192A (zh) * 2018-02-11 2018-08-10 天津大学 基于卷积神经网络的立体图像舒适度评价方法
US11216698B2 (en) * 2018-02-16 2022-01-04 Spirent Communications, Inc. Training a non-reference video scoring system with full reference video scores
US10916003B2 (en) * 2018-03-20 2021-02-09 Uber Technologies, Inc. Image quality scorer machine
US11361416B2 (en) 2018-03-20 2022-06-14 Netflix, Inc. Quantifying encoding comparison metric uncertainty via bootstrapping
CN108259893B (zh) * 2018-03-22 2020-08-18 天津大学 基于双流卷积神经网络的虚拟现实视频质量评价方法
CN108875904A (zh) * 2018-04-04 2018-11-23 北京迈格威科技有限公司 图像处理方法、图像处理装置和计算机可读存储介质
EP3779873A4 (en) * 2018-04-04 2021-06-02 Panasonic Intellectual Property Management Co., Ltd. IMAGE PROCESSING DEVICE AND IMAGE PROCESSING METHOD
CN108596890B (zh) * 2018-04-20 2020-06-16 浙江科技学院 一种基于视觉测量率自适应融合的全参考图像质量客观评价方法
CN108648180B (zh) * 2018-04-20 2020-11-17 浙江科技学院 一种基于视觉多重特征深度融合处理的全参考图像质量客观评价方法
CN108596902B (zh) * 2018-05-04 2020-09-08 北京大学 基于选通卷积神经网络的多任务全参考图像质量评价方法
US10628708B2 (en) * 2018-05-18 2020-04-21 Adobe Inc. Utilizing a deep neural network-based model to identify visually similar digital images based on user-selected visual attributes
CN108665460B (zh) * 2018-05-23 2020-07-03 浙江科技学院 基于组合神经网络和分类神经网络的图像质量评价方法
KR102184755B1 (ko) * 2018-05-31 2020-11-30 서울대학교 산학협력단 안면 특화 초 고화질 심층 신경망 학습 장치 및 방법
CN108986075A (zh) * 2018-06-13 2018-12-11 浙江大华技术股份有限公司 一种优选图像的判断方法及装置
CN109360183B (zh) * 2018-08-20 2021-05-11 中国电子进出口有限公司 一种基于卷积神经网络的人脸图像质量评估方法和系统
US11704791B2 (en) * 2018-08-30 2023-07-18 Topcon Corporation Multivariate and multi-resolution retinal image anomaly detection system
JP6925474B2 (ja) * 2018-08-31 2021-08-25 ソニーセミコンダクタソリューションズ株式会社 固体撮像装置、情報処理システム、固体撮像装置の動作方法及びプログラム
JP6697042B2 (ja) * 2018-08-31 2020-05-20 ソニーセミコンダクタソリューションズ株式会社 固体撮像システム、固体撮像方法及びプログラム
JP7075012B2 (ja) * 2018-09-05 2022-05-25 日本電信電話株式会社 画像処理装置、画像処理方法及び画像処理プログラム
US11055819B1 (en) * 2018-09-27 2021-07-06 Amazon Technologies, Inc. DualPath Deep BackProjection Network for super-resolution
CN111105357B (zh) * 2018-10-25 2023-05-02 杭州海康威视数字技术股份有限公司 一种失真图像的去失真方法、装置及电子设备
US11132586B2 (en) * 2018-10-29 2021-09-28 Nec Corporation Rolling shutter rectification in images/videos using convolutional neural networks with applications to SFM/SLAM with rolling shutter images/videos
CN109685772B (zh) * 2018-12-10 2022-06-14 福州大学 一种基于配准失真表示的无参照立体图像质量评估方法
US11557107B2 (en) 2019-01-02 2023-01-17 Bank Of America Corporation Intelligent recognition and extraction of numerical data from non-numerical graphical representations
CN109801273B (zh) * 2019-01-08 2022-11-01 华侨大学 一种基于极平面线性相似度的光场图像质量评价方法
US10325179B1 (en) * 2019-01-23 2019-06-18 StradVision, Inc. Learning method and learning device for pooling ROI by using masking parameters to be used for mobile devices or compact networks via hardware optimization, and testing method and testing device using the same
CN109871780B (zh) * 2019-01-28 2023-02-10 中国科学院重庆绿色智能技术研究院 一种人脸质量判决方法、系统及人脸识别方法、系统
US11308598B2 (en) * 2019-02-14 2022-04-19 Sharif University Of Technology Quality assessment of an image
US11405695B2 (en) 2019-04-08 2022-08-02 Spirent Communications, Inc. Training an encrypted video stream network scoring system with non-reference video scores
US12335579B2 (en) 2019-04-08 2025-06-17 Spirent Communications, Inc. Cloud gaming benchmark testing
CN110033446B (zh) * 2019-04-10 2022-12-06 西安电子科技大学 基于孪生网络的增强图像质量评价方法
KR102420104B1 (ko) * 2019-05-16 2022-07-12 삼성전자주식회사 영상 처리 장치 및 그 동작방법
KR102825811B1 (ko) 2019-05-21 2025-06-27 삼성전자주식회사 이미지 신호 프로세서의 모델링 방법, 및 전자 기기
US11521011B2 (en) 2019-06-06 2022-12-06 Samsung Electronics Co., Ltd. Method and apparatus for training neural network model for enhancing image detail
CN110517237B (zh) * 2019-08-20 2022-12-06 西安电子科技大学 基于膨胀三维卷积神经网络的无参考视频质量评价方法
CN110766657B (zh) * 2019-09-20 2022-03-18 华中科技大学 一种激光干扰图像质量评价方法
US10877540B2 (en) * 2019-10-04 2020-12-29 Intel Corporation Content adaptive display power savings systems and methods
CN110796651A (zh) * 2019-10-29 2020-02-14 杭州阜博科技有限公司 图像质量的预测方法及装置、电子设备、存储介质
CN110751649B (zh) * 2019-10-29 2021-11-02 腾讯科技(深圳)有限公司 视频质量评估方法、装置、电子设备及存储介质
KR102395038B1 (ko) * 2019-11-20 2022-05-09 한국전자통신연구원 기계 학습 기반 특징과 지식 기반 특징을 이용한 비디오 화질 자동 측정 방법 및 이를 위한 장치
CN111127587B (zh) * 2019-12-16 2023-06-23 杭州电子科技大学 一种基于对抗生成网络的无参考图像质量地图生成方法
CN111192258A (zh) * 2020-01-02 2020-05-22 广州大学 一种图像质量评估方法及装置
CN111524123B (zh) * 2020-04-23 2023-08-08 北京百度网讯科技有限公司 用于处理图像的方法和装置
CN111833326B (zh) * 2020-07-10 2022-02-11 深圳大学 图像质量评价方法、装置、计算机设备及存储介质
US11616959B2 (en) * 2020-07-24 2023-03-28 Ssimwave, Inc. Relationship modeling of encode quality and encode parameters based on source attributes
US11341682B2 (en) * 2020-08-13 2022-05-24 Argo AI, LLC Testing and validation of a camera under electromagnetic interference
KR102801501B1 (ko) 2020-09-29 2025-04-29 삼성전자주식회사 비디오 품질 평가 방법 및 장치
CN112419242B (zh) * 2020-11-10 2023-09-15 西北大学 基于自注意力机制gan网络的无参考图像质量评价方法
CN112330650B (zh) * 2020-11-12 2024-06-28 李庆春 一种检索视频质量评价方法
DE102020216017A1 (de) * 2020-12-16 2022-06-23 Siemens Healthcare Gmbh Bereitstellen von korrigierten medizinischen Bilddaten
CN112784698B (zh) * 2020-12-31 2024-07-02 杭州电子科技大学 基于深层次时空信息的无参考视频质量评价方法
CN112700425B (zh) * 2021-01-07 2024-04-26 云南电网有限责任公司电力科学研究院 一种用于电力设备x射线图像质量的判定方法
US11521639B1 (en) 2021-04-02 2022-12-06 Asapp, Inc. Speech sentiment analysis using a speech sentiment classifier pretrained with pseudo sentiment labels
CN115205188A (zh) * 2021-04-13 2022-10-18 腾讯科技(深圳)有限公司 基于逼近值评估图像视频质量的方法和相关装置
WO2022217496A1 (zh) * 2021-04-14 2022-10-20 中国科学院深圳先进技术研究院 影像数据质量评估方法、装置、终端设备及可读存储介质
CN117157985A (zh) 2021-06-12 2023-12-01 谷歌有限责任公司 用于确定视频内容项的感知质量指标的方法、系统和介质
US20220415037A1 (en) * 2021-06-24 2022-12-29 Meta Platforms, Inc. Video corruption detection
US11763803B1 (en) 2021-07-28 2023-09-19 Asapp, Inc. System, method, and computer program for extracting utterances corresponding to a user problem statement in a conversation between a human agent and a user
CN113505854B (zh) * 2021-07-29 2023-08-15 济南博观智能科技有限公司 一种人脸图像质量评价模型构建方法、装置、设备及介质
KR20230073871A (ko) 2021-11-19 2023-05-26 삼성전자주식회사 영상 처리 장치 및 그 동작 방법
US12067363B1 (en) 2022-02-24 2024-08-20 Asapp, Inc. System, method, and computer program for text sanitization
CN114332088B (zh) * 2022-03-11 2022-06-03 电子科技大学 一种基于运动估计的全参考视频质量评估方法
CN114638793B (zh) * 2022-04-19 2024-07-02 深圳闪回科技有限公司 一种屏幕老化程度检测方法及装置
CN115661114B (zh) * 2022-11-09 2025-03-14 重庆大学 一种基于Conformer和元学习的全参考图像质量评价方法
CN116152183B (zh) * 2023-01-10 2025-09-05 杭州电子科技大学 一种基于失真先验学习的无参考图像质量评价方法
KR102722242B1 (ko) * 2023-02-16 2024-10-24 연세대학교 원주산학협력단 영상의 품질을 정량화하는 방법 및 장치, 그리고 광학 촬영 시스템의 품질을 측정하는 영상 촬영 장치
US20240394835A1 (en) * 2023-05-23 2024-11-28 Constructor Autonomous AG Automatically enhancing image quality in machine learning training dataset by using deep generative models
CN117152092B (zh) * 2023-09-01 2024-05-28 国家广播电视总局广播电视规划院 全参考图像评价方法、装置、电子设备和计算机存储介质
KR20250124609A (ko) * 2024-02-13 2025-08-20 서울대학교산학협력단 근사 알고리즘 기반 umcm 회로를 이용한 컨볼루션 연산 장치 및 그 설계 방법
KR20250138460A (ko) 2024-03-13 2025-09-22 재단법인대구경북과학기술원 비전-언어 모델을 이용한 이미지 추천 장치 및 방법
CN118096770B (zh) * 2024-04-29 2024-06-28 江西财经大学 非视口依赖的抗畸变无参考全景图像质量评价方法与系统
CN118864369B (zh) * 2024-06-28 2025-05-13 宁夏大学 基于多层次特征分布的图像质量评价方法
CN119251341B (zh) * 2024-10-15 2025-11-14 安徽大学 基于泊松流生成模型的ct图像质量评估方法

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
MXPA05004956A (es) 2002-11-06 2005-09-20 Agency Science Tech & Res Un metodo para generar un mapa de significancia orientado a la calidad, para valorar la calidad de una imagen o video.
EP1727088A1 (en) * 2005-05-25 2006-11-29 Thomson Licensing Method for assessing image quality
CN100588271C (zh) 2006-08-08 2010-02-03 安捷伦科技有限公司 基于分组度量和图像度量两者测量视频质量的系统和方法
US8295565B2 (en) * 2007-03-16 2012-10-23 Sti Medical Systems, Llc Method of image quality assessment to produce standardized imaging data
KR101092650B1 (ko) * 2010-01-12 2011-12-13 서강대학교산학협력단 양자화 코드를 이용한 화질 평가 방법 및 장치
RU2431889C1 (ru) * 2010-08-06 2011-10-20 Дмитрий Валерьевич Шмунк Способ суперразрешения изображений и нелинейный цифровой фильтр для его осуществления
CN102497576B (zh) * 2011-12-21 2013-11-20 浙江大学 基于Gabor特征互信息的全参考图像质量评价方法
US8942512B2 (en) * 2011-12-24 2015-01-27 Ecole De Technologie Superieure Methods and systems for processing a first image with reference to a second image
WO2013177779A1 (en) * 2012-05-31 2013-12-05 Thomson Licensing Image quality measurement based on local amplitude and phase spectra
US20150341667A1 (en) 2012-12-21 2015-11-26 Thomson Licensing Video quality model, method for training a video quality model, and method for determining video quality using a video quality model

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