Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: A method and system to avoid flooding a control channel after the end of a system-wide call is provided. A Land Mobile Radio (LMR) site tracks active channel assignments. The LMR site also receives a system-wide call request. Upon receiving the system-wide call request, a snapshot of the channel assignments is taken. When the LMR site receives an indication that the system wide call request has ended, the LMR site receives a de-assign complete message.

Abstract: A method and system to avoid flooding a control channel after the end of a system-wide call is provided. A Land Mobile Radio (LMR) site tracks active channel assignments. The LMR site also receives a system-wide call request. Upon receiving the system-wide call request, a snapshot of the channel assignments is taken. When the LMR site receives an indication that the system wide call request has ended, the LMR site receives a de-assign complete message.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling.

Abstract: Systems and methods of handling packet errors associated with multimedia data received at data receivers from data senders, in which packet error feedback information is provided by the data receivers to the data senders in one or more real-time transport control protocol (RTCP) feedback packets. The data receivers can calculate, determine, or otherwise obtain costs associated with providing specific forms of packet error feedback information in the RTCP feedback packets, and further calculate, determine, or otherwise obtain wait times for handling out-of-order packets, as well as receiving retransmissions of lost packets. By handling packet errors associated with multimedia data while taking into account such costs and wait times, each of which can have an impact on system bandwidth and/or latency, the systems and methods can more reliably achieve the quality of experience (QoE) generally desired and/or required for multimedia data transmissions.

Abstract: Perceptual statistics are used to compute importance maps that indicate which regions of a video frame are important to the human visual system. Importance maps may be generated from encoders that produce motion vectors and employ motion estimation for inter-prediction. The temporal contrast sensitivity function (TCSF) may be computed from the encoder's motion vectors. Quality metrics may be used to construct a true motion vector map (TMVM), which refines the TCSF. Spatial complexity maps (SCMs) can be calculated from simple metrics (e.g. block variance, block luminance, SSIM, and edge detection). Importance maps with TCSF, TMVM, and SCM may be used to modify the standard rate-distortion optimization criterion for selecting the optimum encoding solution. Importance maps may modify encoder quantization. The spatial information for the importance maps may be provided by a lookup table based on block variance, where negative and positive spatial QP offsets for block variances are provided.

Abstract: Perceptual statistics may be used to compute importance maps that indicate which regions of a video frame are important to the human visual system. Importance maps may be applied to the video encoding process to enhance the quality of encoded bitstreams. The temporal contrast sensitivity function (TCSF) may be computed from the encoder's motion vectors. Motion vector quality metrics may be used to construct a true motion vector map (TMVM) that can be used to refine the TCSF. Spatial complexity maps (SCMs) can be calculated from metrics such as block variance, block luminance, SSIM, and edge strength, and the SCMs can be combined with the TCSF to obtain a unified importance map. Importance maps may be used to improve encoding by modifying the criterion for selecting optimum encoding solutions or by modifying the quantization for each target block to be encoded.

Abstract: Systems and methods that employ an application-assisted approach to adaptive spatio-temporal error concealment in video decoders. The systems and methods employ a video receiver that can receive real-time video frames from a video transmitter over a wired and/or wireless network. The video receiver includes an end system configurable as an application for consuming the content of the real-time video frames. The end system application can determine whether each received real-time video frame is complete or incomplete, whether each received slice is complete or incomplete, and provide such information to the video decoder for use in avoiding errors while decoding the respective real-time video frames.

Abstract: Systems and methods of handling packet errors associated with multimedia data received at data receivers from data senders, in which packet error feedback information is provided by the data receivers to the data senders in one or more real-time transport control protocol (RTCP) feedback packets. The data receivers can calculate, determine, or otherwise obtain costs associated with providing specific forms of packet error feedback information in the RTCP feedback packets, and further calculate, determine, or otherwise obtain wait times for handling out-of-order packets, as well as receiving retransmissions of lost packets. By handling packet errors associated with multimedia data while taking into account such costs and wait times, each of which can have an impact on system bandwidth and/or latency, the systems and methods can more reliably achieve the quality of experience (QoE) generally desired and/or required for multimedia data transmissions.

Abstract: Systems and methods of handling packet errors associated with multimedia data received at data receivers from data senders, in which packet error feedback information is provided by the data receivers to the data senders in one or more real-time transport control protocol (RTCP) feedback packets. The data receivers can calculate, determine, or otherwise obtain costs associated with providing specific forms of packet error feedback information in the RTCP feedback packets, and further calculate, determine, or otherwise obtain wait times for handling out-of-order packets, as well as receiving retransmissions of lost packets. By handling packet errors associated with multimedia data while taking into account such costs and wait times, each of which can have an impact on system bandwidth and/or latency, the systems and methods can more reliably achieve the quality of experience (QoE) generally desired and/or required for multimedia data transmissions.

Abstract: Systems and methods of dynamically adapting multimedia data transmit rates of data senders to available bandwidths of data receivers, in which the available bandwidths are estimated by the data senders using at least reception quality feedback information provided in real-time transport control protocol (RTCP) report packets. The data senders can obtain several bandwidth estimation parameters, such as a packet loss ratio and a round trip delay, from the reception quality feedback information, as well as multimedia data transmit rates from the data senders to the data receivers, and obtain estimates of the available bandwidths of the data receivers using at least the bandwidth estimation parameters and multimedia data transmit rates. Further, using the estimated available bandwidth, the data senders can dynamically adapt the multimedia data transmit rates to the available bandwidths for more reliably achieving the quality of experience (QoE) desired and/or required for multimedia data transmissions.

Abstract: Perceptual statistics are used to compute importance maps that indicate which regions of a video frame are important to the human visual system. Importance maps may be generated from encoders that produce motion vectors and employ motion estimation for inter-prediction. The temporal contrast sensitivity function (TCSF) may be computed from the encoder's motion vectors. Quality metrics may be used to construct a true motion vector map (TMVM), which refines the TCSF. Spatial complexity maps (SCMs) can be calculated from simple metrics (e.g. block variance, block luminance, SSIM, and edge detection). Importance maps with TCSF, TMVM, and SCM may be used to modify the standard rate-distortion optimization criterion for selecting the optimum encoding solution. Importance maps may modify encoder quantization. The spatial information for the importance maps may be provided by a lookup table based on block variance, where negative and positive spatial QP offsets for block variances are provided.

Abstract: Perceptual statistics may be used to compute importance maps that indicate which regions of a video frame are important to the human visual system. Importance maps may be applied to the video encoding process to enhance the quality of encoded bitstreams. The temporal contrast sensitivity function (TCSF) may be computed from the encoder's motion vectors. Motion vector quality metrics may be used to construct a true motion vector map (TMVM) that can be used to refine the TCSF. Spatial complexity maps (SCMs) can be calculated from metrics such as block variance, block luminance, SSIM, and edge strength, and the SCMs can be combined with the TCSF to obtain a unified importance map. Importance maps may be used to improve encoding by modifying the criterion for selecting optimum encoding solutions or by modifying the quantization for each target block to be encoded.

Abstract: Systems and methods of video conferencing, in which existing video encoders associated with a multipoint control unit (MCU) can be shared among conference participant devices, depending upon the availability of processor and/or memory resources for the MCU. In making decisions regarding the sharing of video encoders, the systems and methods take into account various parameters associated with potential conference participant devices and the total cost of the processor/memory resources for the MCU, to determine whether a video encoder of the MCU can be shared with, or allocated to, the respective conference participant devices. By determining whether to share or allocate a video encoder of the MCU, depending upon the available processor/memory resources for the MCU, the MCU can be made to operate with increased stability. The systems and methods can be applied to any application that sends the same content to multiple receivers, including announcement and video streaming applications.