No reference realtime video quality assessment
AssigneeiStreamPlanet Co., LLC
InventorsNachiketa Mishra, Steven Roeder
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Abstract
A method for video quality assessment implemented by a computing device, where the method includes selecting a first set of frames from an input video source, determining features of the first set of frames for input into a first machine learning model, applying the first machine learning model to the features of the first set of frames to obtain a video quality score and a confidence score, and triggering a retraining of the first machine learning model in response to the confidence score being determined to be out of bounds.
Claims20
A method for video quality assessment implemented by a computing device, the method comprising:
- selecting a first set of frames from an input video source;
- determining features of the first set of frames for input into a first machine learning model;
- applying the first machine learning model to the features of the first set of frames to obtain a video quality score and a confidence score that indicates a confidence level of the video quality score;
- and
- triggering a retraining of the first machine learning model in response to the confidence score being determined to be out of bounds indicating a low probability of accuracy of the video quality score, and checking for further frames from the input video sources for the video quality assessment in response to the confidence score being determined to be within a defined range of the confidence score,
- wherein frames within the first set of frames are to be distributed from the input video source to a content delivery network.
A non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, will cause said processor to perform operations of video quality assessment comprising:
- selecting a first set of frames from an input video source;
- determining features of the first set of frames for input into a first machine learning model;
- applying the first machine learning model to the features of the first set of frames to obtain a video quality score and a confidence score that indicates a confidence level of the video quality score;
- and
- triggering a retraining of the first machine learning model in response to the confidence score being determined to be out of bounds indicating a low probability of accuracy of the video quality score, and checking for further frames from the input video sources to perform the operations of the video quality assessment in response to the confidence score being determined to be within a defined range of the confidence score;
- wherein frames within the first set of frames are to be distributed from the input video source to a content delivery network.
An electronic device comprising:
- a non-transitory machine-readable storage medium having stored therein a video quality assessor;
- and
- a processor coupled to the non-transitory machine-readable storage medium, the processor to perform operations of video quality assessment, the operations are to select a first set of frames from an input video source, to determine features of the first set of frames for input into a first machine learning model, to apply the first machine learning model to the features of the first set of frame to obtain a video quality score and a confidence score that indicates a confidence level of the video quality score, and to trigger a retraining of the first machine learning model in response to the confidence score being determined to be out of bounds indicating a low probability of accuracy of the video quality score, and check for further frames from the input video sources to perform the operations of the video quality assessment in response to the confidence score being determined to be within a defined range of the confidence score, wherein frames within the first set of frames are to be distributed from the input video source to a content delivery network.
Dependencies are inferred from the claim text.