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GPU-based complete hardware transcoding method and system

A hardware and complete technology, applied in electrical components, image communication, selective content distribution, etc., can solve the problems of consuming system time, not fully utilizing GPU computing power, and consuming large CPU and memory resources

Active Publication Date: 2016-01-06
CHINANETCENT TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Another common commercial transcoding software, WOWZA, is not pure GPU hardware transcoding. They all use the method of "GPU decoding, memory retrieval, CPU transcoding, pushing video memory, GPU encoding". The consumption is still very large. At the same time, during the transcoding process, the data going back and forth between the memory and the video memory consumes a lot of system time, and the transcoding calculation does not fully utilize the computing power of the GPU.
Therefore, the existing NVENC and transcoding software such as FFMPEG and WOWZA that apply NVENC are not strictly GPU-based hardware transcoding

Method used

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  • GPU-based complete hardware transcoding method and system
  • GPU-based complete hardware transcoding method and system

Examples

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example 1

[0048] Example 1: If the hardware structure meets the following conditions: (1) The computing power of the CPU is weak, or the CPU needs to be used for computing other more important tasks at the same time; (2) the computing power of the GPU meets the basic requirements for using NVENC. In such an environment, video transcoding will naturally reduce CPU usage, make full use of GPU computing power, and improve transcoding efficiency. If the input video format to be transcoded belongs to MPEG-2 or H.264, or the YUV4:4:4 format that has been decoded and generated by other decoders, and the output format is H.264, then the engineering requirements at this time are most suitable for use. The method and system are implemented.

example 2

[0049] Example 2: If the hardware structure meets the following conditions: the computing power of GPU is much stronger than that of CPU, and the format requirements of video transcoding meet the format requirements of Example 1. At this time, this method and system can be used to achieve the highest efficiency, and at the same time, the The computing power of the redundant CPU is transferred to other needs.

example 3

[0050] Example 3: for video source format stability (MPEG-2 or H.264), output video format does not have special requirement or can accept the video website or individual of H.264 format, can utilize this method and system to obtain or provide efficient, stable Video transcoding service.

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Abstract

The present application relates to a GPU (Graphics Processing Unit)-based complete hardware transcoding method and system. Specifically, in the method and the system of the present application, hardware transcoding is carried out by means of the OPENCV (Open Source Computer Vision Library) packaged CUVID on the base of the existing NVENC; then, transcoding calculation is carried out in a video memory by adopting a newly developed transcoding logic; and finally coding is carried out by using a coder of the NVENC, so that a complete hardware transcoding process by only using a GPU is achieved.

Description

technical field [0001] This patent relates to streaming media video transcoding technology, especially for a GPU-based video data with H.264, MPEG-2 encoding format for complete hardware transcoding to generate H.264 encoding format video to reduce CPU usage rate methods and systems. Background technique [0002] In 2007, NVIDIA launched the CUDA (Compute Unified Device Architecture) framework and the CUDAC language, pushing the traditional GPU general-purpose computing (GPGPU) to a new peak. Traditional GPU general-purpose computing achieves the purpose of general-purpose computing by "deceiving" the rendering process of GPU and video memory, with the help of the graphics card's powerful ability to process matrix data in parallel. This process is neither intuitive nor labor-intensive, and it is difficult to get a good promotion. The emergence of the CUDA framework makes GPGPU programming similar to ordinary high-level language programming, enabling developers to convenien...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N21/4402
CPCH04N21/4402H04N21/440218H04N21/440263
Inventor 洪珂白永光王荣祥
Owner CHINANETCENT TECH
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