WO2018170393A3 - Frame interpolation via adaptive convolution and adaptive separable convolution - Google Patents

Frame interpolation via adaptive convolution and adaptive separable convolution Download PDF

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Publication number
WO2018170393A3
WO2018170393A3 PCT/US2018/022858 US2018022858W WO2018170393A3 WO 2018170393 A3 WO2018170393 A3 WO 2018170393A3 US 2018022858 W US2018022858 W US 2018022858W WO 2018170393 A3 WO2018170393 A3 WO 2018170393A3
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WO
WIPO (PCT)
Prior art keywords
convolution
pixel
frame
adaptive
patch
Prior art date
Application number
PCT/US2018/022858
Other languages
French (fr)
Other versions
WO2018170393A9 (en
WO2018170393A2 (en
Inventor
Feng Liu
Simon NIKLAUS
Long MAI
Original Assignee
Portland State University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US201762473234P priority Critical
Priority to US62/473,234 priority
Priority to US201762485794P priority
Priority to US62/485,794 priority
Application filed by Portland State University filed Critical Portland State University
Publication of WO2018170393A2 publication Critical patent/WO2018170393A2/en
Publication of WO2018170393A9 publication Critical patent/WO2018170393A9/en
Publication of WO2018170393A3 publication Critical patent/WO2018170393A3/en

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/01—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level
    • H04N7/0127—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level by changing the field or frame frequency of the incoming video signal, e.g. frame rate converter
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4046—Scaling the whole image or part thereof using neural networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/01—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level
    • H04N7/0135—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level involving interpolation processes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/587—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal sub-sampling or interpolation, e.g. decimation or subsequent interpolation of pictures in a video sequence

Abstract

Systems, methods, and computer-readable media for context-aware synthesis for video frame interpolation are provided. A convolutional neural network (ConvNet) may, given two input video or image frames, interpolate a frame temporarily in the middle of the two input frames by combining motion estimation and pixel synthesis into a single step and formulating pixel interpolation as a local convolution over patches in the input images. The ConvNet may estimate a convolution kernel based on a first receptive field patch of a first input image frame and a second receptive field patch of a second input image frame. The ConvNet may then convolve the convolutional kernel over a first pixel patch of the first input image frame and a second pixel patch of the second input image frame to obtain color data of an output pixel of the interpolation frame. Other embodiments may be described and/or claimed.
PCT/US2018/022858 2017-03-17 2018-03-16 Frame interpolation via adaptive convolution and adaptive separable convolution WO2018170393A2 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
US201762473234P true 2017-03-17 2017-03-17
US62/473,234 2017-03-17
US201762485794P true 2017-04-14 2017-04-14
US62/485,794 2017-04-14

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR1020197030137A KR20190132415A (en) 2017-03-17 2018-03-16 Frame Interpolation with Adaptive Convolution and Adaptive Isolated Convolution
US16/495,029 US20200012940A1 (en) 2017-03-17 2018-03-16 Frame interpolation via adaptive convolution and adaptive separable convolution

Publications (3)

Publication Number Publication Date
WO2018170393A2 WO2018170393A2 (en) 2018-09-20
WO2018170393A9 WO2018170393A9 (en) 2018-11-15
WO2018170393A3 true WO2018170393A3 (en) 2018-12-20

Family

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PCT/US2018/022858 WO2018170393A2 (en) 2017-03-17 2018-03-16 Frame interpolation via adaptive convolution and adaptive separable convolution

Country Status (3)

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US (1) US20200012940A1 (en)
KR (1) KR20190132415A (en)
WO (1) WO2018170393A2 (en)

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US10706890B2 (en) * 2017-08-24 2020-07-07 Intel Corporation Cinematic space-time view synthesis for enhanced viewing experiences in computing environments
US10776688B2 (en) 2017-11-06 2020-09-15 Nvidia Corporation Multi-frame video interpolation using optical flow
US11122238B1 (en) * 2017-11-07 2021-09-14 Twitter, Inc. Frame interpolation with multi-scale deep loss functions and generative adversarial networks
US11019355B2 (en) * 2018-04-03 2021-05-25 Electronics And Telecommunications Research Institute Inter-prediction method and apparatus using reference frame generated based on deep learning
US10984245B1 (en) * 2018-06-11 2021-04-20 Facebook, Inc. Convolutional neural network based on groupwise convolution for efficient video analysis
US11126915B2 (en) * 2018-10-15 2021-09-21 Sony Corporation Information processing apparatus and information processing method for volume data visualization
CN111178491A (en) * 2018-11-09 2020-05-19 佳能株式会社 Method, device, system and storage medium for training and applying neural network model
CN109472315B (en) * 2018-11-15 2021-09-24 江苏木盟智能科技有限公司 Target detection method and system based on depth separable convolution
KR20200071404A (en) * 2018-12-11 2020-06-19 삼성전자주식회사 Image processing apparatus and operating method for the same
US11080835B2 (en) 2019-01-09 2021-08-03 Disney Enterprises, Inc. Pixel error detection system
CN109905624B (en) * 2019-03-01 2020-10-16 北京大学深圳研究生院 Video frame interpolation method, device and equipment
WO2020216438A1 (en) * 2019-04-23 2020-10-29 Telefonaktiebolaget Lm Ericsson (Publ) A computer software module, a device and a method for accelerating inference for compressed videos
CN110111366B (en) * 2019-05-06 2021-04-30 北京理工大学 End-to-end optical flow estimation method based on multistage loss
US10896356B2 (en) * 2019-05-10 2021-01-19 Samsung Electronics Co., Ltd. Efficient CNN-based solution for video frame interpolation
CN110427094B (en) * 2019-07-17 2021-08-17 Oppo广东移动通信有限公司 Display method, display device, electronic equipment and computer readable medium
WO2021063119A1 (en) * 2019-10-01 2021-04-08 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Method and apparatus for image processing, terminal
US10958869B1 (en) 2019-11-14 2021-03-23 Huawei Technologies Co., Ltd. System, device and method for video frame interpolation using a structured neural network
KR102207736B1 (en) * 2020-01-14 2021-01-26 한국과학기술원 Frame interpolation, apparatus and method using deep neural network
CN111212287A (en) * 2020-01-15 2020-05-29 济南浪潮高新科技投资发展有限公司 Video compression method based on image interpolation method
US11074730B1 (en) 2020-01-23 2021-07-27 Netapp, Inc. Augmented reality diagnostic tool for data center nodes

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Patent Citations (1)

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WO2016132146A1 (en) * 2015-02-19 2016-08-25 Magic Pony Technology Limited Visual processing using sub-pixel convolutions

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
GUCAN LONG ET AL.: "Learning Image Matching by Simply Watching Video", ECCV 2016, 2016, pages 434 - 450, XP047355274 *
HAITAM BEN YAHIA: "Frame Interpolation using Convolutional Neural Networks on 2D animation", BACHELOR THESIS, 24 June 2016 (2016-06-24), XP055558906 *

Also Published As

Publication number Publication date
US20200012940A1 (en) 2020-01-09
WO2018170393A9 (en) 2018-11-15
WO2018170393A2 (en) 2018-09-20
KR20190132415A (en) 2019-11-27

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