Video frame rate upconversion method and system based on cyclic convolution neural network

Abstract

The invention provides a video frame rate upconversion method and a video frame rate upconversion system based on a cyclic convolution neural network. Dividing the initial video into groups of image blocks comprising two consecutive frames of images; taking two consecutive frames of images in the image block as inputs of a target cyclic convolution neural network, and synthesizing intermediate frame images corresponding to the two consecutive frames of images; wherein, the target cyclic convolution neural network is trained by a preset training data set. The intermediate frame image is inserted into the image block to obtain the target video after the video frame rate upconversion. Thus, the mapping from the front and back two frames to the middle frame can be completed, the frame rate ofthe original video is improved, and the 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 video frame rate up-conversion method and system based on a circular convolutional neural network. Background technique [0002] The frame rate of a video is one of the important criteria to measure the video quality. The frame rate of the video represents the number of frames displayed per second, and the higher the frame rate, the better the smoothness of the video image. In addition, because the transmission of network video is affected by the network environment, in low-bandwidth video transmission, the video generally undergoes a certain degree of frame rate down-sampling and compression processing, so as to transmit at a lower bit rate. Therefore, at the receiving end, it is necessary to restore the frame rate of the original video, that is, to insert one or more frames between two frames of the original video, thereby increasing the frame rate of the vi...

AI Technical Summary

Problems solved by technology

[0004] However, the quality of intermediate frames is largely affected by the accuracy of motion estimation

For the video frame rate up-conversion technology, the generated frames obtained by using the deep learning algorithm still have problems such as excessive smoothing and missing details.

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