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Video frame rate up-conversion method and system based on convolutional neural network

A technology of convolutional neural network and video frame rate, which is applied in the field of video frame rate up-conversion method and system, can solve the problems of missing details and excessive smoothing of generated frames, so as to improve accuracy, improve video frame rate, and improve visual effects Effect

Active Publication Date: 2019-02-22
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0004] However, the quality of the generated intermediate frame is largely affected by the accuracy of the motion vector. The generated frame obtained by the deep learning algorithm has problems such as excessive smoothing and missing details.

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  • Video frame rate up-conversion method and system based on convolutional neural network
  • Video frame rate up-conversion method and system based on convolutional neural network
  • Video frame rate up-conversion method and system based on convolutional neural network

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Embodiment Construction

[0047] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0048] Such as figure 1 As shown, the video frame rate up-conversion method based on the convolutional neural network provided by the present invention may include the following steps:

[0049] S1: Read the original high frame rate video, cut it into multiple groups of image blocks of three consecutive frames, where the two frames before and after are used as input, and the middle frame is used as a label to form a supervised data set for training and steps in step S3 Verification in S4;

[0050] S2: ...

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Abstract

The invention provides a video frame rate up-conversion method and system based on a convolutional neural network. The method includes: receiving an initial video transmitted by a sending end; dividing the initial video into multiple image blocks of which each comprises two consecutive frame images; using the two consecutive frame images in the image block as input of the target convolutional-neural-network, and synthesizing an intermediate frame corresponding to the two consecutive frame images, wherein the target convolutional-neural-network is obtained through training of a preset trainingdata set, and includes an encoder, a decoder and an optical-flow prediction layer; and inserting the intermediate frame image into the image block to obtain a target video after video frame rate up-conversion. Therefore, mapping from a previous frame and a next frame to the intermediate frame can be completed, a frame rate of the original video is improved, and up-conversion of the video frame rate is better completed.

Description

technical field [0001] The present invention relates to the technical field of video processing, in particular to a convolutional neural network-based video frame rate up-conversion method and system. Background technique [0002] With the rapid development of television broadcasting, the Internet and the film industry, video has become one of the indispensable ways of entertainment for people. At the same time, on the premise of satisfying the demand for video content, people's pursuit of video quality is also constantly improving. Video frame rate, bit rate and resolution are important criteria for measuring video quality. The frame rate of a video represents the number of frames displayed per second, which directly affects the smoothness of the video image. The higher the video frame rate, the better the smoothness of the picture, and the less jumpy. In addition, the transmission of network video is affected by the network environment. In low-bandwidth video transmiss...

Claims

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Application Information

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IPC IPC(8): H04N7/01G06N3/04
CPCH04N7/0127H04N7/014G06N3/045
Inventor 宋利张智峰解蓉陈立
Owner SHANGHAI JIAO TONG UNIV
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