Abstract: Objects within an image are assigned a semantic interest that is indicative of their importance to the image as a whole. The objects are detected automatically, and sub-images that span each of the objects are excerpted from the image. An image embedding is determined for each of the sub-images as well as for the whole image by using an image encoder implemented as a trained multi-modal neural network. The degree of similarity between the image embeddings of a sub-image and that of the whole image is used as a measure of the semantic importance of the object portrayed in the sub-image. Objects of high semantic importance comprise the semantic regions of interest of the image. Knowledge of such regions may be used to enhance and improve the efficiency of downstream image-processing tasks, such as image compression, pan and scan, and contrast enhancement.

Abstract: A large language model (LLM) is used to broaden a search for supporting material for a media project. The LLM is provided with contextual material from the media project and optional grounding material and generates a list of types of supporting material. The list is provided to a machine-learning-based encoder, which encodes the list items into embedding space vectors. A body of source material, which may multimodal, is also encoded into the embedding space. A search engine is used to locate items of source material having embedded space vectors closest to vectors corresponding to the list of types of supporting material. Located items are provided to a media editing application as suggested supporting material. The body of source material may include live external sources. A small language model fine-tuned with a training data set generated by a LLM may be used instead of the LLM.

Abstract: Objects within an image are assigned a semantic interest that is indicative of their importance to the image as a whole. The objects are detected automatically, and sub-images that span each of the objects are excerpted from the image. An image embedding is determined for each of the sub-images as well as for the whole image by using an image encoder implemented as a trained multi-modal neural network. The degree of similarity between the image embeddings of a sub-image and that of the whole image is used as a measure of the semantic importance of the object portrayed in the sub-image. Objects of high semantic importance comprise the semantic regions of interest of the image. Knowledge of such regions may be used to enhance and improve the efficiency of downstream image-processing tasks, such as image compression, pan and scan, and contrast enhancement.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Efficient processing of chroma-subsampled video is performed using convolutional neural networks (CNNs) in which the luma and chroma channels are processed separately. The luma channel is independently convolved and downsampled and, in parallel, the chroma channels are convolved and then merged with the downsampled luma to generate encoded chroma-subsampled video. Further processing of the encoded video that involves deconvolution and upsampling, splitting into two sets of channels, and further deconvolutions and upsampling is used in CNNs to generate decoded chroma-subsampled video in compression-decompression applications, to remove noise from chroma-subsampled video, or to upsample chroma-subsampled video to RGB 444 video. CNNs with separate luma and chroma processing in which the further processing includes additional convolutions and downsampling may be used for object recognition and semantic search in chroma-subsampled video.

Abstract: Efficient processing of chroma-subsampled video is performed using convolutional neural networks (CNNs) in which the luma and chroma channels are processed separately. The luma channel is independently convolved and downsampled and, in parallel, the chroma channels are convolved and then merged with the downsampled luma to generate encoded chroma-subsampled video. Further processing of the encoded video that involves deconvolution and upsampling, splitting into two sets of channels, and further deconvolutions and upsampling is used in CNNs to generate decoded chroma-subsampled video in compression-decompression applications, to remove noise from chroma-subsampled video, or to upsample chroma-subsampled video to RGB 444 video. CNNs with separate luma and chroma processing in which the further processing includes additional convolutions and downsampling may be used for object recognition and semantic search in chroma-subsampled video.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: A computer-based method for media composition of a family of related time-based media programs. The method involves creating a master program with time-based elements of video and/or audio as well as time-based and non-time-based metadata, creating a derivative program that includes derivative elements, defining an inheritance relationship between the master program and the derivative program that specifies elements of the master program to be inherited by the derivative program, and causing the derivative program to inherit the specified elements from the master program in accordance with the inheritance relationship. User interfaces are provided for creating, editing, and viewing hierarchical trees of related programs.

Abstract: A computer-based method for media composition of a family of related time-based media programs. The method involves creating a master program with time-based elements of video and/or audio as well as time-based and non-time-based metadata, creating a derivative program that includes derivative elements, defining an inheritance relationship between the master program and the derivative program that specifies elements of the master program to be inherited by the derivative program, and causing the derivative program to inherit the specified elements from the master program in accordance with the inheritance relationship. User interfaces are provided for creating, editing, and viewing hierarchical trees of related programs.

Abstract: A computer-based method for media composition of a family of related time-based media programs. The method involves creating a master program with time-based elements of video and/or audio as well as time-based and non-time-based metadata, creating a derivative program that includes derivative elements, defining an inheritance relationship between the master program and the derivative program that specifies elements of the master program to be inherited by the derivative program, and causing the derivative program to inherit the specified elements from the master program in accordance with the inheritance relationship. User interfaces are provided for creating, editing, and viewing hierarchical trees of related programs.

Abstract: A computer-based method for media composition of a family of related time-based media programs. The method involves creating a master program with time-based elements of video and/or audio as well as time-based and non-time-based metadata, creating a derivative program that includes derivative elements, defining an inheritance relationship between the master program and the derivative program that specifies elements of the master program to be inherited by the derivative program, and causing the derivative program to inherit the specified elements from the master program in accordance with the inheritance relationship. User interfaces are provided for creating, editing, and viewing hierarchical trees of related programs.

Abstract: A system and method for generating a representation of a color modification to be applied to segments on a digital nonlinear editing system, where each segment is a component of a media composition, and represents a section of a digital media. An indication of a modification to be applied to a color attribute of a segment is received, and the source from which the segment originates is identified. The indication of the color modification is then stored, and, as a result, the color modification is applied to other segments that originate from the identified source. Source color modification is applied to a section of a digital media on a digital nonlinear editing system. a media segment represents the section of a digital media. The segment is a component of a media composition, and originates from a source data structure. The source data structure also represents the section of the digital media. The section is received, and the first source data structure from which the segment originates is identified.