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A High Frame Rate Video Reproduction Method Based on Grid Structure Deep Learning

A technology of deep learning and grid structure, applied in the direction of neural learning method, neural architecture, interpolation processing conversion, etc., can solve the problems of unsatisfactory performance, blurred and disordered synthesis results, etc.

Active Publication Date: 2021-08-31
福建帝视科技集团有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

High frame rate reproduction algorithm based on optical flow estimation For video scenes with motion blur and fast motion, it is difficult to estimate a very accurate optical flow
In addition, the performance of the spatial adaptive convolution method for video scenes with occluders is not satisfactory, and the synthesis results are usually blurred and disordered

Method used

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  • A High Frame Rate Video Reproduction Method Based on Grid Structure Deep Learning
  • A High Frame Rate Video Reproduction Method Based on Grid Structure Deep Learning
  • A High Frame Rate Video Reproduction Method Based on Grid Structure Deep Learning

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Embodiment Construction

[0058] like Figure 1-4 As shown in one of them, the present invention discloses a method for reproducing video at a high frame rate based on grid structure deep learning, which is divided into the following steps:

[0059] Step 0, image selection for training database. The training data set of this patent is the UCF-101 action data set [5] , which covers more than 10,000 action videos. We randomly sample the video, and select high-quality video frames with obvious motion (the selection criterion of the present invention is to consider PSNR greater than 35 as high-quality images). Finally, 24,000 sets of video frames were selected, and each set consisted of three consecutive images.

[0060] Step 1, the production of the training database, resets the image size of the selected training data. First set the original image uniformly to the size of H*W, then normalize the image to the [-1,1] interval, and finally form a paired set containing N images where c∈{1,2,…,N}, H is ...

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Abstract

The invention discloses a method for reproducing video at a high frame rate based on deep learning of a grid structure. The three-dimensional pixel stream estimated by using the grid structure can obtain relatively accurate results in motion scenes with various amounts of motion. The method of the present invention is more robust than the prior art. In order to further improve the accuracy of the 3D pixel stream and the effect of high frame rate reproduction, the present invention proposes a combination of convolutional feature extraction layer and grid network structure. The high frame rate reproduction result obtained by the method of the present invention is more delicate and real in the detailed texture of the synthesized frame than other prior art.

Description

technical field [0001] The invention relates to the field of video high frame rate reproduction, in particular to a method for video high frame rate reproduction based on grid structure deep learning. Background technique [0002] Video high frame rate reproduction is to use the video image information of adjacent frames in the video sequence to estimate the key frame in the middle, which belongs to a classic image processing problem. In general, video high frame rate reproduction algorithms can be divided into interpolated frames and extrapolated frames. The former is to use the information of two consecutive frames of images to estimate the key frame in the middle; the latter is to use the information of two consecutive frames of video images in the video sequence to estimate the previous frame or the next frame. [0003] According to the continuous video image information in the video sequence, the video high frame rate reproduction algorithm is a method to reasonably us...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T7/207G06T17/20G06N3/04G06N3/08H04N7/01
CPCH04N7/0135G06N3/084G06T7/207G06T17/20G06N3/045G06T5/73
Inventor 刘文哲李根童同高钦泉
Owner 福建帝视科技集团有限公司