![]() ![]() The only other currently viable option is Intel’s integrated GPU (iGPU) accelerator known as QuickSync. From here on out, we’ll just refer to it as “NVENC.” Simply put, it’s a very common and easy solution to enable hardware transcoding. This is what Nvidia calls its decoding and encoding engines. Most often, Nvidia GPUs are often recommended for hardware transcoding via NVDEC/NVENC. More importantly, however - hardware transcoding has more limitations, considerations, and compatibility issues than software transcoding. Generally, hardware transcoding is faster and more efficient than software transcoding. This reduces load on the CPU, allowing you to allocate its resources to other things such as Dockers, VMs, or other applications. However, when someone talks about hardware transcoding, they mean using a dedicated piece of hardware (typically a GPU) to accelerate the process. Technically, all transcoding is software in the end. Note: certain file formats like VC1 are limited to single threaded transcoding only. (think of dual CPU systems like the Lego build and the Anniversary build) It’s a multi-threaded process which can take advantage of not only multiple threads, but also multiple phyiscal CPUs. Typically this is accomplished via software run on the CPU of the Plex server. Think of decoding as taking the file apart, and encoding as putting it back together. Simply put, it’s the combination of two processes: decoding and encoding. You’re looking for more transcoding power (this does not affect direct play or direct stream).You have an existing NAS with all of your media on it (this guide will use Unraid as an example).The hardware portion of this guide assumes: Ultimately, the recommendation will be to add a dedicated transcoding box that uses Intel QuickSync. The guide will also provide recommendations for adding hardware transcoding to your existing setup. WorkSurface->Info.CropW, workSurface->Info.The purpose of this guide is to give an overview of hardware transcoding with Plex. MSG(cerr Data.Y, workSurface->Data.UV, workSurface->Data.Pitch, The function named cvtBGRtoTwoPlaneYUV() does the conversion from BGR->YUV.(TAKES 200 MS ) bool VideoWriter_IntelMFX::write_one(cv::InputArray bgr) Okay, so this is the write_one() function from OpenCV, it converts the frame to YUV and encodes it.This function is callled in every frame. I was thinking the encoding would take time, but apparently conversion part takes a lot more, which is odd. ![]() The encoding function takes about 0.045(~20FPS) seconds. The conversion function BGR->YUV takes about 0.20 seconds(~5 FPS). That computer only has intel integrated GPU.(so, Using FFMPEG with NVIDIA GPU is not possible.) Also, in my case, there is one dedicated computer and camera for this software. However, The problem is, I want to get 10 FPS from my video.(I mean the video eventually becomes 10FPS, but while encoding process, it doesn't get all the frames.(I am getting in between 3-5 FPS).When I dig the code I have realized that, the encoding function is okay but the function that converts BGR->YUV is pretty slow. I want to develop one software that takes frames from 4K camera and encodes it simultaneously.Īnd right-now I can get the frames and compress it to a. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |