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
input
adaptive
Prior art date
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PCT/US2018/022858
Other languages
French (fr)
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WO2018170393A9 (en
WO2018170393A2 (en
Inventor
Feng Liu
Simon NIKLAUS
Long MAI
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Portland State University
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Publication date
Application filed by Portland State University filed Critical Portland State University
Priority to US16/495,029 priority Critical patent/US11468318B2/en
Priority to KR1020197030137A priority patent/KR102474168B1/en
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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    • 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 OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS 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 OR 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 OR 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 (2)

Application Number Priority Date Filing Date Title
US16/495,029 US11468318B2 (en) 2017-03-17 2018-03-16 Frame interpolation via adaptive convolution and adaptive separable convolution
KR1020197030137A KR102474168B1 (en) 2017-03-17 2018-03-16 Frame interpolation with adaptive convolution and adaptive disjoint convolution

Applications Claiming Priority (4)

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

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

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

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US (1) US11468318B2 (en)
KR (1) KR102474168B1 (en)
WO (1) WO2018170393A2 (en)

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Also Published As

Publication number Publication date
US20200012940A1 (en) 2020-01-09
US11468318B2 (en) 2022-10-11
KR102474168B1 (en) 2022-12-06
KR20190132415A (en) 2019-11-27
WO2018170393A9 (en) 2018-11-15
WO2018170393A2 (en) 2018-09-20

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