WO2018170393A3 - Frame interpolation via adaptive convolution and adaptive separable convolution - Google Patents
Frame interpolation via adaptive convolution and adaptive separable convolution Download PDFInfo
- 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
Links
- 230000003044 adaptive Effects 0.000 title 2
- 230000015572 biosynthetic process Effects 0.000 abstract 2
- 238000003786 synthesis reaction Methods 0.000 abstract 2
- 230000002194 synthesizing Effects 0.000 abstract 2
- 230000001537 neural Effects 0.000 abstract 1
Classifications
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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
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- 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
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- 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.
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
ID=63522622
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2018/022858 WO2018170393A2 (en) | 2017-03-17 | 2018-03-16 | Frame interpolation via adaptive convolution and adaptive separable convolution |
Country Status (3)
Country | Link |
---|---|
US (1) | US20200012940A1 (en) |
KR (1) | KR20190132415A (en) |
WO (1) | WO2018170393A2 (en) |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10776688B2 (en) | 2017-11-06 | 2020-09-15 | Nvidia Corporation | Multi-frame video interpolation using optical flow |
KR20200071404A (en) * | 2018-12-11 | 2020-06-19 | 삼성전자주식회사 | Image processing apparatus and operating method for the same |
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 |
CN110111366A (en) * | 2019-05-06 | 2019-08-09 | 北京理工大学 | A kind of end-to-end light stream estimation method based on multistage loss amount |
US10896356B2 (en) * | 2019-05-10 | 2021-01-19 | Samsung Electronics Co., Ltd. | Efficient CNN-based solution for video frame interpolation |
CN110427094A (en) * | 2019-07-17 | 2019-11-08 | Oppo广东移动通信有限公司 | Display methods, device, electronic equipment and computer-readable medium |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016132146A1 (en) * | 2015-02-19 | 2016-08-25 | Magic Pony Technology Limited | Visual processing using sub-pixel convolutions |
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2018
- 2018-03-16 US US16/495,029 patent/US20200012940A1/en active Pending
- 2018-03-16 KR KR1020197030137A patent/KR20190132415A/en active Search and Examination
- 2018-03-16 WO PCT/US2018/022858 patent/WO2018170393A2/en active Application Filing
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016132146A1 (en) * | 2015-02-19 | 2016-08-25 | Magic Pony Technology Limited | Visual processing using sub-pixel convolutions |
Non-Patent Citations (2)
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 |
---|---|
KR20190132415A (en) | 2019-11-27 |
WO2018170393A2 (en) | 2018-09-20 |
US20200012940A1 (en) | 2020-01-09 |
WO2018170393A9 (en) | 2018-11-15 |
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