A video transcoding method, device and system
A video transcoding and video technology, applied in the field of video processing, can solve the problems of increasing the number of frames from pictures to video images, not supporting video input and output, and unable to use video, etc., and achieve the effect of reducing deployment costs
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Embodiment 1
[0053] refer to Figure 4 , which shows a flow chart of the steps of an embodiment of a video transcoding method of the present application, which may specifically include the following steps:
[0054] Step 402, receiving the model file issued by the machine learning framework;
[0055] Preferably, the machine learning framework can use any one of Theano library, Caffe library or Torch library to train samples so as to generate template files.
[0056] It should be noted here that the model file can be obtained through training with various existing machine learning frameworks, and the embodiment of the present application does not limit the specific machine learning framework used.
[0057] In addition, the model file is obtained by offline training of the machine learning framework, which is completely independent from the actual video processing process. In this way, the template file can be generated through the most suitable machine learning framework according to differ...
Embodiment 2
[0072] refer to Figure 5 , showing a flow chart of the steps of another video transcoding method embodiment of the present application, which may specifically include the following steps:
[0073] Step 500, train the training samples through the machine learning framework to obtain the model file.
[0074] Preferably, the step of obtaining the model file by training the training sample through the machine learning framework may further include:
[0075] S 510. Collect training samples based on video processing requirements;
[0076] Preferably, it is based on the user's specific requirements for video enhancement, such as super-resolution, denoising, and the like. The training samples may be a large number of pictures collected, such as tens of thousands of pictures.
[0077] S 520. Perform offline training on the training samples to obtain a model file.
[0078] Preferably, the machine learning framework can use any one of Theano library, Caffe library or Torch library t...
Embodiment 3
[0097] In the following, a specific application of the embodiment of the present application will be described in conjunction with an actual business scenario.
[0098] Realize the function of converting video to high-definition service on MTS. In specific video processing, for example, if a user wants to remake a cartoon in high-definition, he can put forward the demand for enhancement of the cartoon video through MTS online, for example, to The video is super-resolutioned, that is, the cartoon is converted into a high-definition video.
[0099] After collecting tens of thousands of pictures based on user needs, the machine learning framework runs a specific algorithm to perform offline training on tens of thousands of pictures to obtain corresponding model files. Since the training process is completely independent from the actual video enhancement process, the training machine can be directly deployed in the experimental environment.
[0100] In the specific application of...
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