Patents by Inventor Andy SCHULER
Andy SCHULER has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10475172Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.Type: GrantFiled: June 25, 2018Date of Patent: November 12, 2019Assignee: NETFLIX, INC.Inventors: Anne Aaron, Dae Kim, Yu-Chieh Lin, David Ronca, Andy Schuler, Kuyen Tsao, Chi-Hao Wu
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Patent number: 10438335Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.Type: GrantFiled: June 25, 2018Date of Patent: October 8, 2019Assignee: NETFLIX, INC.Inventors: Anne Aaron, Dae Kim, Yu-Chieh Lin, David Ronca, Andy Schuler, Kuyen Tsao, Chi-Hao Wu
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Patent number: 10404986Abstract: In one embodiment of the present invention, an encoding bitrate ladder selector tailors bitrate ladders to the complexity of source data. Upon receiving source data, a complexity analyzer configures an encoder to repeatedly encode the source data-setting a constant quantization parameter to a different value for each encode. The complexity analyzer processes the encoding results to determine an equation that relates a visual quality metric to an encoding bitrate. The bucketing unit solves this equation to estimate a bucketing bitrate at a predetermined value of the visual quality metric. Based on the bucketing bitrate, the bucketing unit assigns the source data to a complexity bucket having an associated, predetermined bitrate ladder. Advantageously, sagaciously selecting the bitrate ladder enables encoding that optimally reflects tradeoffs between quality and resources (e.g., storage and bandwidth) across a variety of source data types instead of a single, “typical” source data type.Type: GrantFiled: March 30, 2015Date of Patent: September 3, 2019Assignee: NETFLIX, INC.Inventors: Anne Aaron, David Ronca, Ioannis Katsavounidis, Andy Schuler
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Publication number: 20180300869Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.Type: ApplicationFiled: June 25, 2018Publication date: October 18, 2018Inventors: Anne AARON, Dae KIM, Yu-Chieh LIN, David RONCA, Andy SCHULER, Kuyen TSAO, Chi-Hao WU
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Patent number: 10007977Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.Type: GrantFiled: May 11, 2015Date of Patent: June 26, 2018Assignee: NETFLIX, INC.Inventors: Anne Aaron, Dae Kim, Yu-Chieh Lin, David Ronca, Andy Schuler, Kuyen Tsao, Chi-Hao Wu
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Publication number: 20160335754Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.Type: ApplicationFiled: May 11, 2015Publication date: November 17, 2016Inventors: Anne Aaron, Dae Kim, Yu-Chieh Lin, David Ronca, Andy Schuler, Kuyen Tsao, Chi-Hao Wu
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Publication number: 20160295216Abstract: In one embodiment of the present invention, an encoding bitrate ladder selector tailors bitrate ladders to the complexity of source data. Upon receiving source data, a complexity analyzer configures an encoder to repeatedly encode the source data-setting a constant quantization parameter to a different value for each encode. The complexity analyzer processes the encoding results to determine an equation that relates a visual quality metric to an encoding bitrate. The bucketing unit solves this equation to estimate a bucketing bitrate at a predetermined value of the visual quality metric. Based on the bucketing bitrate, the bucketing unit assigns the source data to a complexity bucket having an associated, predetermined bitrate ladder. Advantageously, sagaciously selecting the bitrate ladder enables encoding that optimally reflects tradeoffs between quality and resources (e.g., storage and bandwidth) across a variety of source data types instead of a single, “typical” source data type.Type: ApplicationFiled: March 30, 2015Publication date: October 6, 2016Inventors: Anne AARON, David RONCA, Ioannis KATSAVOUNIDIS, Andy SCHULER