Video processing method, and model construction method

A processing method and video technology, applied in the field of processing methods and model construction, can solve problems such as poor reconstruction of high dynamic range video effects, and achieve the effect of solving poor effects and avoiding ghosting phenomenon

Pending Publication Date: 2022-01-11
ALIBABA GRP HLDG LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide a video processing method and a model construction method to at least solve the technical problem of poor reconstruction of high dynamic range video in the prior art

Method used

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  • Video processing method, and model construction method
  • Video processing method, and model construction method
  • Video processing method, and model construction method

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Experimental program
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Embodiment 1

[0036] According to an embodiment of the present invention, an embodiment of a method for constructing a model is provided. It should be noted that the steps shown in the flowcharts of the drawings can be executed in a computer system such as a set of computer-executable instructions, and, although A logical order is shown in the flowcharts, but in some cases the steps shown or described may be performed in an order different from that shown or described herein.

[0037] The method embodiment provided in Embodiment 1 of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. figure 1 It shows a hardware structure block diagram of a computer terminal (or mobile device) for realizing the construction method of the model. Such as figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more (shown by 102a, 102b, ..., 102n in the figure) processor 102 (the processor 102 may include but not limit...

Embodiment 2

[0072] This application provides Figure 5 The processing method shown in the video. Figure 5 It is a flowchart of a video processing method according to Embodiment 2 of the present invention.

[0073] Step S501, receiving a service invocation request sent by a client, wherein the service invocation request carries video data satisfying a first preset condition and a video sequence satisfying a second preset condition.

[0074] Step S502, performing machine learning training on the video data satisfying the first preset condition and the video sequences satisfying the second preset condition.

[0075] The above-mentioned video sequence of the second preset condition is a low dynamic range video sequence.

[0076] Step S503, outputting a training result, wherein the training result is a set of model parameters.

[0077] Optionally, the method further includes: packaging the model parameter set and sending it to the client. The parameters in the model parameter set are used...

Embodiment 3

[0084] Embodiments of the present invention may provide a computer terminal, and the computer terminal may be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the foregoing computer terminal may also be replaced with a terminal device such as a mobile terminal.

[0085] Optionally, in this embodiment, the foregoing computer terminal may be located in at least one network device among multiple network devices of the computer network.

[0086] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for constructing the model of the application program: acquire video data satisfying the first preset condition; perform alignment processing on the video sequences in the video data; aligning the processed video sequences to obtain video sequences satisfying the second preset condition under different exposure durations; inputting the video sequence satisfying the second ...

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PUM

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Abstract

The invention discloses a video processing method, and a model construction method. The model construction method comprises the following steps: acquiring video data meeting a first preset condition; performing alignment processing on video sequences in the video data; processing the aligned video sequences to obtain video sequences meeting a second preset condition under different exposure durations; inputting the video sequences meeting the second preset condition and the video data meeting the first preset condition into a deep convolutional neural network model for training to obtain a target model. According to the invention, the technical problem of poor effect of reconstructing a high dynamic range video in the prior art is solved.

Description

technical field [0001] The present invention relates to the technical field of video processing, in particular, to a video processing method and a model building method. Background technique [0002] High Dynamic Range (Hereinafter referred to as HDR) video, compared with Standard Dynamic Range (Hereinafter referred to as SDR) video, the light and dark levels of the image are clearer, the image details are richer, and the real scene can be reproduced more realistically . With the development of HDR technology and the gradual popularization of HDR displays, the demand for HDR video is gradually increasing. True HDR video production requires the use of high dynamic range imaging devices at the acquisition end, and the use of HDR non-editing software during production, which means that HDR video content production has high requirements for shooting equipment and pre-processing technology. Therefore, the current HDR content is still relatively scarce. [0003] The current HDR...

Claims

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Application Information

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IPC IPC(8): G06T5/50G06N3/04G06N3/08
CPCG06T5/50G06N3/084G06T2207/10016G06T2207/20208G06T2207/20084G06T2207/20081G06N3/045
Inventor 陈冠英
Owner ALIBABA GRP HLDG LTD
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