46 baris
2.0 KiB
ReStructuredText
46 baris
2.0 KiB
ReStructuredText
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.. meta::
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:description: Video decoding pipeline
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:keywords: video decoder, video decoding, rocDecode, AMD, ROCm
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********************************************************************
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Video decoding pipeline
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********************************************************************
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.. image:: ../data/VideoDecoderPipelinetest.png
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:alt: Video decoder pipeline test
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There are three main components in rocDecode:
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* Demuxer: Our demuxer is based on FFmpeg, a leading multimedia framework. For more information,
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refer to the `FFmpeg website <https://ffmpeg.org/about.html>`_.
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* Video parser APIs
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* Video decoder APIs
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rocDecode follows this workflow:
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1. The demuxer extracts a segment of video data and sends it to the video parser.
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2. The video parser extracts crucial information, such as picture and slice parameters, and sends it to
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the decoder APIs.
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3. The hardware receives the picture and slice parameters, then decodes a frame using Video
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Acceleration API (VA-API).
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4. This process repeats in a loop until all frames have been decoded.
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Steps in decoding video content for applications (available in the rocDecode Toolkit):
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1. Demultiplex the content into elementary stream packets (FFmpeg)
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2. Parse the demultiplexed packets into video frames for the decoder provided by rocDecode API.
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3. Decode compressed video frames into YUV frames using rocDecode API.
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4. Wait for the decoding to finish.
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5. Get the decoded YUV frame from amd-gpu context to HIP (using VAAPI-HIP interoperability under
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ROCm).
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6. Run HIP kernels in the mapped YUV frame. For example, format conversion, scaling, object detection,
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classification, and others.
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7. Release the decoded frame.
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.. note::
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YUV is a color space that represents images using luminance (Y) for brightness and two chrominance
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components (U and V) for color information.
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The preceding steps are demonstrated in the sample applications located in our
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`GitHub repository <https://github.com/ROCm/rocDecode/tree/develop/samples>`_ directory.
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