Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing adaptive audio bitrate selection. An example embodiment operates by receiving a data streaming request. The embodiment then streams data at a first audio bitrate. The embodiment then determines whether there is a streaming discontinuity associated with the data. The embodiment then determines, in response to the determination that there is a streaming discontinuity, whether a network condition satisfies one or more conditions based on one or more streaming parameters. The embodiment then, in response to the determination that the network condition satisfies the one or more conditions, adjusts the first audio bitrate to a second audio bitrate during the streaming discontinuity.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: A client device receives a broadcast content signal containing an interactive identifier over a managed network at a client device. The interactive identifier may be a trigger that is included in a header or embedded within the digital video data. The trigger may have a temporal component, wherein the trigger can expire after a certain period of time. In response to identification of the trigger, the client device sends a user request for interactive content over an unmanaged network. For example, the managed network may be a one-way satellite television network, IP-television network or cable television network and the unmanaged network may be the Internet. The client device switches between receiving data from the managed network to receiving data from the unmanaged network.

Abstract: Systems and methods are presented that permit an individual to share digital video between video devices using a cable television system. A first video device streams digital video to a cable headend, which transcodes it and stitches it with other video content, such as a menuing system. The headend then transmits the digital video to a second video device, such as a set top box, for display. The data stream may be controlled using a standard set top box remote control, and the system may be used without purchasing additional hardware or software, or configuring a wireless local area network.

Abstract: Systems and methods are presented that permit an individual to share digital video between video devices using a cable television system. A first video device streams digital video to a cable headend, which transcodes it and stitches it with other video content, such as a menuing system. The headend then transmits the digital video to a second video device, such as a set top box, for display. The data stream may be controlled using a standard set top box remote control, and the system may be used without purchasing additional hardware or software, or configuring a wireless local area network.

Abstract: A client device receives a broadcast content signal containing an interactive identifier over a managed network at a client device. The interactive identifier may be a trigger that is included in a header or embedded within the digital video data. The trigger may have a temporal component, wherein the trigger can expire after a certain period of time. In response to identification of the trigger, the client device sends a user request for interactive content over an unmanaged network. For example, the managed network may be a one-way satellite television network, IP-television network or cable television network and the unmanaged network may be the Internet. The client device switches between receiving data from the managed network to receiving data from the unmanaged network.

Abstract: A client device receives a broadcast content signal containing an interactive identifier over a managed network at a client device. The interactive identifier may be a trigger that is included in a header or embedded within the digital video data. The trigger may have a temporal component, wherein the trigger can expire after a certain period of time. In response to identification of the trigger, the client device sends a user request for interactive content over an unmanaged network. For example, the managed network may be a one-way satellite television network, IP-television network or cable television network and the unmanaged network may be the Internet. The client device switches between receiving data from the managed network to receiving data from the unmanaged network.

Abstract: A method and apparatus for compressing video signals. The video compression may include row by row horizontal subband filtering of video data representing part of an image, and row by row concurrent vertical high and vertical low pass subband filtering of the results of the horizontal subband filtering. Less than the vertical filter length of rows of results of the horizontal subband filtering may be buffered at a time.

Abstract: A method and apparatus for object based motion compensation forms objects from blocks of an input image based on motion similarity of the blocks in the input image and a reference image. In addition, object motion compensation vectors for each formed object and object relative block motion compensation vectors for each object's constituent blocks relative to the object's motion compensation vector are computed.

Abstract: A method and apparatus for object based motion compensation forms objects from blocks of an input image based on motion similarity of the blocks in the input image and a reference image. In addition, object motion compensation vectors for each formed object and object relative block motion compensation vectors for each object's constituent blocks relative to the object's motion compensation vector are computed.

Abstract: A method and apparatus for performing video compression and decompression using dynamic quantization and/or encoding. According to one aspect of the compression method, an image is recursively filtered into its constituent components each represented by a matrix of coefficients. Next, the level of correlation of a first of the matrices of coefficients is determined. Based on the level of correlation of this first coefficient matrix, one of a first quantization technique and a second quantization technique is selected. The first coefficient matrix is then quantized using the selected quantization technique. According to another aspect of the compression method, an image is digitally filtered into its constituent components, each represented by a matrix of coefficients. At least certain matrices are quantized to generated a first quantized matrix.