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.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
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 ...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


