Method for improving frame rate up conversion effect by using generated network

Abstract

The invention discloses a method for improving frame rate up conversion effect by using a generated network. The method comprises the following steps of 1, comprising an original video via JM18.6 to acquire the compressed video, then reserving odd-number frames in the compressed video, and reconstructing even-number frames to acquire reconstructed frames of the even-number frames by using a framerate up conversion algorithm based on direction selection movement estimation; 2, building a generated network G, inputting the reconstructed frames IE into a neural network for training, comparing the corresponding frames IP in the original video with network output, backwards propagating an error, and acquire the optimal remediation effect after multiple times of adjustment; 3, repeatedly executing the steps 1 and 2 until an error variation rate is invariable or less than a set threshold; and 4, applying the trained network, inputting the reconstructed frames has been subjected to frame rateup conversion into the network to acquire an image with high quality. According to the method provided by the invention, psnr values and quality of the reconstructed frames are improved, and thus theframe rate up conversion effect is improved.

Description

technical field [0001] The invention relates to the fields of deep neural network and frame rate up-conversion, in particular to CNN structure network and residual structure. technical background [0002] Video information is one of the most important information sources for human beings, and occupies a large proportion in the field of signal processing. The essence of the existing video source is the result of spatial and temporal sampling after the active three-dimensional scene is projected onto a two-dimensional plane. Frame rate up-conversion, also known as time interpolation, is an important part of video format conversion technology. It is mainly used to realize the conversion between different frame rate display formats. For example, if a movie film shot at 24 frames per second is played on a high-definition television above 60 frames per second, frame rate up-conversion must be used to convert between the two formats. In low-bit-rate video communication, it is of...

AI Technical Summary

Problems solved by technology

In addition, the image pixels obtained by the traditional technology using MSE (mean square error) as the loss function are too smooth, and the local texture details are less. Although the low-quality images on PSNR have been greatly improved, the visual experience There is no expected difference, but an adversarial identification model is constructed in SRGAN, and the traditional loss function is abandoned and the content loss function and the adversarial loss function are combined into a perceptual loss function, which makes the generated image texture details more, Let people have a stronger feeling in visual perception

Images