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.
Claims14
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;
- determining whether the segment comprises a first scene segment;
- training a deep neural network (DNN) for each representation of the segment, including a first number of epochs when the segment comprises the first scene segment and a second number of epochs when the segment does not comprise the first scene segment, wherein the second number of epochs is less than the first number of epochs, the training 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;
- determining whether the segment comprises a first scene segment;
- training a deep neural network (DNN) for each representation of the segment, including a first number of epochs when the segment comprises the first scene segment and a second number of epochs when the segment does not comprise the first scene segment, wherein the second number of epochs is less than the first number of epochs, the training 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.