Scalable Per-Title Encoding
AssigneeBitmovin, Inc.
InventorsHadi Amirpour, Christian Timmerer
Life of this patent
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Abstract
A scalable per-title encoding technique may include detecting scene cuts in an input video received by an encoding network or system, generating segments of the input video, performing per-title encoding of a segment of the input video, training a deep neural network (DNN) for each representation of the segment, thereby generating a trained DNN, compressing the trained DNN, thereby generating a compressed trained DNN, and generating an enhanced bitrate ladder including metadata comprising the compressed trained DNN. In some embodiments, the method also may include generating a base layer bitrate ladder for CPU devices, and providing the enhanced bitrate ladder for GPU-available devices.
Claims18
A method for scalable per-title encoding, the method comprising:
- receiving an input video;
- detecting scene cuts in the input video;
- generating a plurality of segments of the input video;
- performing per-title encoding of a segment of the plurality of segments of the input video;
- training a deep neural network (DNN) for each representation of the segment, resulting in a trained DNN;
- compressing the trained DNN, resulting in a compressed trained DNN;
- and
- generating an enhanced bitrate ladder including metadata comprising the compressed trained DNN.
A non-transitory computer-readable medium storing computer instructions for scalable per-title encoding that when executed on one or more computer processors perform the steps of:
- receiving an input video;
- detecting scene cuts in the input video;
- generating a plurality of segments of the input video;
- performing per-title encoding of a segment of the plurality of segments of the input video;
- training a deep neural network (DNN) for each representation of the segment, resulting in a trained DNN;
- compressing the trained DNN, resulting in a compressed trained DNN;
- and
- generating an enhanced bitrate ladder including metadata comprising the compressed trained DNN.
Dependencies are inferred from the claim text.