Abstract: Classifying video for encoding optimization may include computing a content complexity score of a video, the content complexity score indicating a measure of how detailed the video is in terms of spatial and temporal information, categorizing the video into one of a plurality of buckets according to the content complexity score, each bucket representing a category of video content having a different range of content complexity scores and being associated with a ladder specific to the range, and encoding the video according to the ladder of the one of the plurality of buckets into which the video is categorized.

Abstract: Classifying video for encoding optimization may include computing a content complexity score of a video, the content complexity score indicating a measure of how detailed the video is in terms of spatial and temporal information, categorizing the video into one of a plurality of buckets according to the content complexity score, each bucket representing a category of video content having a different range of content complexity scores and being associated with a ladder specific to the range, and encoding the video according to the ladder of the one of the plurality of buckets into which the video is categorized.

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: Aspects relate to a spectral modeling system for building chemometrics (calibration) models for spectral devices targeting ultra-wide-scale deployment. The spectral modeling system includes a spectral converter for generating a plurality of artificial spectra using spectral data of a plurality of samples measured by a subset of a plurality of spectral devices and spectral device characteristics representing spectral variations in the plurality of spectral devices. The spectral modeling system further includes a chemometrics engine for generating a chemometrics model for one or more parameters associated with the plurality of samples based on the spectral data and the plurality of artificial spectra.

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: Real-time latency of audio/video streams is identified. Signatures of a reference audio/video stream and signatures of a test audio/video stream are buffered. A needle is constructed as a vector including a set of signatures of the reference audio/video stream. Correlations of the needle to successive vectors of sets of signatures of the test audio/video stream are computed using a correlation function that calculates relatedness of the needle vector to each of the successive vectors of the test audio/video stream. A synchronization offset is identified between the test stream and the reference stream according to a maximum correlation point of the correlations of the needle to the successive sets of signatures of the test audio/video stream. The reference audio/video stream and the test audio/video stream are aligned according to the synchronization offset.

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: Banding effects in an image or video are assessed. The image or each frame of a video is decomposed into luminance or color channels. Signal activity is computed at each spatial location in each channel. A significance of the signal activity at each spatial location is determined by comparing each element of the signal activity with a significance threshold. Banding pixels are detected as those pixels of the image or frame having significant signal activity at their respective spatial locations and having non-significant signal activity of at least a minimum threshold percentage of neighboring pixels to the respective spatial locations.

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: Detection of scaling of compressed videos or images is provided. A frequency domain transformation is applied along both horizontal and vertical directions of input video or images to generate frequency domain data. Statistics in the frequency domain data are computed for each of the horizontal and vertical directions to extract features. The features are modeled to scores along each of the horizontal and vertical directions. An original resolution of the input video or images in the horizontal and vertical directions is identified according to the scores.

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: Banding effects in an image or video are assessed. The image or each frame of a video is decomposed into luminance or color channels. Signal activity is computed at each spatial location in each channel. A significance of the signal activity at each spatial location is determined by comparing each element of the signal activity with a significance threshold. Banding pixels are detected as those pixels of the image or frame having significant signal activity at their respective spatial locations and having non-significant signal activity of at least a minimum threshold percentage of neighboring pixels to the respective spatial locations.

Abstract: Real-time latency of audio/video streams is identified. Signatures of a reference audio/video stream and signatures of a test audio/video stream are buffered. A needle is constructed as a vector including a set of signatures of the reference audio/video stream. Correlations of the needle to successive vectors of sets of signatures of the test audio/video stream are computed using a correlation function that calculates relatedness of the needle vector to each of the successive vectors of the test audio/video stream. A synchronization offset is identified between the test stream and the reference stream according to a maximum correlation point of the correlations of the needle to the successive sets of signatures of the test audio/video stream. The reference audio/video stream and the test audio/video stream are aligned according to the synchronization offset.

Abstract: Classifying video for encoding optimization may include computing a content complexity score of a video, the content complexity score indicating a measure of how detailed the video is in terms of spatial and temporal information, categorizing the video into one of a plurality of buckets according to the content complexity score, each bucket representing a category of video content having a different range of content complexity scores and being associated with a ladder specific to the range, and encoding the video according to the ladder of the one of the plurality of buckets into which the video is categorized.

Abstract: No-reference (NR) quality assessment (VQA) of a test visual media input encoding media content is provided. The test video visual media input is decomposed into multiple-channel representations. Domain knowledge is obtained by performing content analysis, distortion analysis, human visual system (HVS) modeling, and/or viewing device analysis. The multiple-channel representations are passed into deep neural networks (DNNs) producing DNN outputs. The DNN outputs are combined using domain knowledge to produce an overall quality score of the test visual media input.

Abstract: Various fentanyl derivatives are described. The derivatives exhibit high binding affinity to opioid receptors, but also have decreased blood brain permeability as compared to standard fentanyl. This, the resulting derivatives can be less addictive than standard fentanyl. Methods of making such derivatives are also disclosed in this document.

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: In various implementations, a method includes determining a sequence of source packets. In some implementations, the sequence of source packets satisfies a windowing condition. In various implementations, the method includes synthesizing a first set of one or more parity packets as a function of a first set of source packets in the sequence. In some implementations, the first set of source packets satisfies a first encoding pattern. In various implementations, the method includes synthesizing a second set of parity packets as a function of a second set of source packets in the sequence. In some implementations, the second set of source packets satisfies a second encoding pattern that is different from the first encoding pattern. In some implementations, the first and second encoding patterns characterize an encoding structure determined as a function of a channel characterization vector.

Abstract: An endpoint device receives a sequence of audio frames. The endpoint device determines for each audio frame a respective importance level among possible importance levels ranging from a low importance level to a high importance level based on content in the audio frame indicative of the respective importance level. The endpoint device associates each audio frame with the respective importance level, to produce different subsets of audio frames associated with respective ones of different importance levels. The endpoint device, for each subset of audio frames, applies forward error correction to a fraction of audio frames in the subset of audio frames, wherein the fraction increases as the importance level of the audio frames in the subset increases, and does not apply forward error correction to remaining audio frames in the subset.

Abstract: An endpoint device receives a sequence of audio frames. The endpoint device determines for each audio frame a respective importance level among possible importance levels ranging from a low importance level to a high importance level based on content in the audio frame indicative of the respective importance level. The endpoint device associates each audio frame with the respective importance level, to produce different subsets of audio frames associated with respective ones of different importance levels. The endpoint device, for each subset of audio frames, applies forward error correction to a fraction of audio frames in the subset of audio frames, wherein the fraction increases as the importance level of the audio frames in the subset increases, and does not apply forward error correction to remaining audio frames in the subset.