Video transcoding method and device based on geometric generation model, medium and equipment

Abstract

The invention discloses a video transcoding method and device based on a geometric generation model, a medium and equipment, and the method comprises the steps: constructing the geometric generation model, and obtaining a super-resolution reconstruction model after training; obtaining an MPEG-4 video; decoding the MPEG-4 video, and storing the decoded MPEG-4 video in a continuous static picture form; for each frame of picture obtained after the MPEG-4 video is decoded, performing super-resolution amplification reconstruction on each frame of picture through a super-resolution reconstruction model; and encoding the image after super-resolution amplification and reconstruction into a video in an H.265 format. According to the method, the problem of mode collapse in the generative adversarial network is solved from the perspective of geometry through the geometric generative model, adversarial learning is avoided, and the generated image is richer and more vivid.

Description

technical field [0001] The present invention relates to a video transcoding method, in particular to a video transcoding method, device, medium and equipment based on a geometric generation model. Background technique [0002] With the widespread popularity of the Internet, the Internet has increasingly become the main way for us to obtain information in our daily life. With the rapid rise of industries such as online video and live broadcast, people have put forward higher requirements for the quality of video, and the demand for high-definition or even ultra-high-definition video getting bigger. In recent years, domestic 4K and 8K video devices have been sought after by the public, but there are not enough ultra-high-definition video resources on the Internet, so these ultra-high-definition video devices can only play ordinary high-definition videos most of the time, and cannot take advantage of their hardware. The lack of ultra-high-definition video resources is due to t...

AI Technical Summary

Problems solved by technology

Among them, the super-resolution method based on the generative confrontation network (GAN) can generate more realistic and clear texture and other details, so it is widely used in the field of image generation. More commonly used are super-resolution, image translation and human pose generation, etc., but In practical applications, confrontation learning is required between the discriminator D and the generator G. If the data distribution has multiple clusters or is distributed in multiple isolated manifolds, it is difficult for the generator to learn multiple patterns well, and it is easy to Phenomena that cause GAN mode collapse and convergence difficulties

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