Video enhancement and transmission method based on deep learning

A technology of deep learning and video enhancement, which is applied in the field of video enhancement and transmission based on deep learning, can solve the problem that the reconstruction effect needs to be improved, the non-linear mapping relationship between low-resolution images and high-resolution images cannot be fully expressed and reconstructed, and the occupation of transmission bandwidth consumption etc.

Inactive Publication Date: 2017-05-31
福建帝视信息科技有限公司
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AI Technical Summary

Problems solved by technology

Its disadvantage is that during video decoding, the reduced-resolution video frame and the representative high-resolution patch frame need to be sent to the receiver together, which will additionally increase the bandwidth consumption of video transmission
Its disadvantages are: it needs to disseminate over-complete dictionaries and low-res

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  • Video enhancement and transmission method based on deep learning
  • Video enhancement and transmission method based on deep learning
  • Video enhancement and transmission method based on deep learning

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[0044] Such as figure 1 As shown, for the user’s high-definition video experience in the traditional video transmission technology, the high-definition video is usually collected at the video source, and the high-definition video is directly transmitted during the video transmission process. The user terminal uses a video decoder to decode the received high-definition video and send it to the terminal. Is displayed on the screen for users to watch.

[0045] Such as Figure 2-8 As shown in one, the present invention also discloses a video enhancement and transmission method based on deep learning. During the video transmission process, low-definition video is used for transmission, and the user terminal reconstructs the low-definition video using super-resolution image reconstruction technology based on deep learning. To play high-definition videos, which can effectively reduce bandwidth costs, the video transmission method includes the following steps:

[0046] a) At the video sou...

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Abstract

The invention discloses a video enhancement and transmission method based on deep learning. Downsampling is carried out on a high-definition video at a video source end, thereby obtaining a low-definition video; the low-definition video is compressed in an existing video coding mode; and the compressed low-definition video is transmitted. The size of the video data is greatly reduced through downsampling and video coding, so the video traffic needing to be transmitted is correspondingly reduced, and an effect of reducing the bandwidth cost is achieved. At a user receiving end, a user receives the low-definition video, reconstructs the low-definition video by employing a super-resolution image reconstruction method of the deep learning and restores the low-definition video into a high-resolution video for the user to watch, so the video transmission bandwidth cost is effectively reduced. According to the method, the video transmission bandwidth cost is reduced by at least 50%; the resolution of the live broadcast video is improved; and the watching experience of a user is improved.

Description

technical field [0001] The invention relates to computer artificial intelligence technology, and is applied in the field of video enhancement and transmission, in particular to a video enhancement and transmission method based on deep learning. Background technique [0002] According to the Internet industry report survey, by 2020, 80% of the data on the Internet will be video data. Although the computing power of mobile terminals has been greatly improved and the 4G network is becoming more and more perfect, the explosive growth of video data has brought new challenges to video storage and transmission on the mobile Internet. Taking live video platforms as an example, there are currently nearly 300 online live broadcast platforms in China, and dozens of investment institutions have participated in the investment. Among them, the market size of online live broadcasts is about 15 billion, and the number of users watching them has exceeded 200 million. According to the foreca...

Claims

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Application Information

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IPC IPC(8): H04N21/2343H04N21/4402H04N19/59
CPCH04N21/234363H04N19/59H04N21/440263
Inventor 童同高钦泉
Owner 福建帝视信息科技有限公司
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