Abstract: Candidate encodes of a source video are produced using a plurality of encoding configurations. Quality of experience (QoE) scores for each of the candidate encodes are computed. The QoE scores of the plurality of candidate encodes are compared to determine a lowest bitrate encode that achieve a target QoE score. The lowest bitrate encode that achieve the target QoE score as an optimized output video is selected.

Abstract: Candidate encodes of a source video are produced using a plurality of encoding configurations. Quality of experience (QoE) scores for each of the candidate encodes are computed. The QoE scores of the plurality of candidate encodes are compared to determine a lowest bitrate encode that achieve a target QoE score. The lowest bitrate encode that achieve the target QoE score as an optimized output video is selected.

Abstract: A source quality of a source video and a source content complexity of the source video are identified. Parameter constraints with respect to parameters of an operation are received. The source video quality, source content complexity, and parameter constraints are applied to a deep neural network (DNN) producing DNN outputs. In an example, the DNN outputs are combined using domain knowledge to provide the filter parameters, as predicted, to a filter chain, such that applying the filter chain to the input source video results in an output video achieving the full reference video quality score. In another example, the DNN outputs are combined using domain knowledge to provide the filter parameters, as predicted, to a filter chain, such that applying the filter chain to the input source video results in an output video achieving the full reference video quality score.

Abstract: Correlated quality-of-experience (QoE) and latency measures are generated at a plurality of monitoring points along a multimedia delivery chain including multiple video operations. At each of the plurality of monitoring points, an absolute QoE measure defined on a human perceptual quality scale for media content is computed, and one or more of content extraction or feature extraction on the media content are performed. To a common middleware from each of the plurality of monitoring points, the respective QoE measure and results of the one or more of content extraction or feature extraction are transmitted. Computing and updating an absolute QoE measure for each of the plurality of monitoring points is performed. Computing and updating latencies between multiple monitoring points at the middleware using the results from each of the plurality of monitoring points is also performed.

Abstract: Correlated quality-of-experience (QoE) and latency measures are generated at a plurality of monitoring points along a multimedia delivery chain including multiple video operations. At each of the plurality of monitoring points, an absolute QoE measure defined on a human perceptual quality scale for media content is computed, and one or more of content extraction or feature extraction on the media content are performed. To a common middleware from each of the plurality of monitoring points, the respective QoE measure and results of the one or more of content extraction or feature extraction are transmitted. Computing and updating an absolute QoE measure for each of the plurality of monitoring points is performed. Computing and updating latencies between multiple monitoring points at the middleware using the results from each of the plurality of monitoring points is also performed.

Abstract: A source quality of a source video and a source content complexity of the source video are identified. Parameter constraints with respect to parameters of an operation are received. The source video quality, source content complexity, and parameter constraints are applied to a deep neural network (DNN) producing DNN outputs. In an example, the DNN outputs are combined using domain knowledge to provide the filter parameters, as predicted, to a filter chain, such that applying the filter chain to the input source video results in an output video achieving the full reference video quality score. In another example, the DNN outputs are combined using domain knowledge to provide the filter parameters, as predicted, to a filter chain, such that applying the filter chain to the input source video results in an output video achieving the full reference video quality score.

Abstract: Candidate encodes of a source video are produced using a plurality of encoding configurations. Quality of experience (QoE) scores for each of the candidate encodes are computed. The QoE scores of the plurality of candidate encodes are compared to determine a lowest bitrate encode that achieve a target QoE score. The lowest bitrate encode that achieve the target QoE score as an optimized output video is selected.

Abstract: Correlated quality-of-experience (QoE) and latency measures are generated at a plurality of monitoring points along a multimedia delivery chain including multiple video operations. At each of the plurality of monitoring points, an absolute QoE measure defined on a human perceptual quality scale for media content is computed, and one or more of content extraction or feature extraction on the media content are performed. To a common middleware from each of the plurality of monitoring points, the respective QoE measure and results of the one or more of content extraction or feature extraction are transmitted. Computing and updating an absolute QoE measure for each of the plurality of monitoring points is performed. Computing and updating latencies between multiple monitoring points at the middleware using the results from each of the plurality of monitoring points is also performed.

Abstract: A system and method of improving signal intelligibility over an interference signal is provided. The system includes a psychoacoustic professor having a psychoacoustic model wherein the level of a signal-of-interest is improved so as to be audible above noise and so as not to exceed a predetermined maximum output level. The system can be combined with active noise cancellation.

Abstract: A sound intelligibility enhancement (SIE) system is disclosed. The SIE system uses a psychoacoustic model and preferably an oversampled filterbank wherein the level of a signal-of-interest that falls below the environmental noise is selectively amplified as a function of the input level and frequency so that it is audible above the noise but never exceeds a predetermined maximum output level as a function of frequency. The SIE system can be combined with active noise cancellation.