Methods and systems are provided for integrated channel and/or band stacking solutions. A plurality of signals may be received, such as via a signal receiver, with each of the received signals being different from remaining ones of the plurality signals. At least two received signals may be processed, such as via one or more processing circuits, and an output signal may be generated based on the processing of the at least two received signals. The output signal may include only one or more portions from each of the at least two signals, with the one or more portions being stacked within the output signal. The stacking of the one or more portions from the at least two signals may include applying channel equalization, with the channel equalization including equalizing power of a plurality of sub-components of a frequency band corresponding to the one or more portions.

Methods and systems are provided for integrated channel and/or band stacking solutions. A plurality of signals may be received, such as via a signal receiver, with each of the received signals being different from remaining ones of the plurality signals. At least two received signals may be processed, such as via one or more processing circuits, and an output signal may be generated based on the processing of the at least two received signals. The output signal may include only one or more portions from each of the at least two signals, with the one or more portions being stacked within the output signal. The stacking of the one or more portions from the at least two signals may include applying channel equalization, with the channel equalization including equalizing power of a plurality of sub-components of a frequency band corresponding to the one or more portions.

A computer-implemented method for a multiple imager imaging system is disclosed. The method comprises capturing regular images; capturing depth sensor images; and performing a depth-based alignment operation to adjust factory alignment parameters based on depth sensor information.

A display apparatus including a display unit, a control apparatus, and a method are provided. The display apparatus includes a controller is configured to obtain a status signal from the control apparatus, to select, based on the obtained status signal, a mode from among a user interface (UI) display limit mode and a UI display mode, wherein the UI display limit mode limits displaying a UI to control an output from the display apparatus on the display unit and the UI display mode does not limit displaying the UI on the display unit, and to operate the display unit in the selected mode.

A display apparatus including a display unit, a control apparatus, and a method are provided. The display apparatus includes a controller is configured to obtain a status signal from the control apparatus, to select, based on the obtained status signal, a mode from among a user interface (UI) display limit mode and a UI display mode, wherein the UI display limit mode limits displaying a UI to control an output from the display apparatus on the display unit and the UI display mode does not limit displaying the UI on the display unit, and to operate the display unit in the selected mode.

A video quality evaluation system comprises a training module, a calculation module, an analytical module, a designing module, an optimization module, and an estimation module. The training module collects training videos and trains labels of perceptual quality generation that is associated with the collected videos. The calculation module determines objective metrics based on the trained labels that are associated with the collected videos. The analytical module analyses scenes of the training video, and correlates objective metrics associated with the analysed scenes using perceptual parameters. The designing module designs a Convolutional Neural Network (CNN) Architecture based on data associated with the objective metrics and trains a model generated based on the designed CNN architecture. The optimization module optimizes the model and optimizes power after the model optimization. The estimation module estimates perceptual quality scores for incoming video data after the power optimization.

A method, apparatus, and system for detecting DeepFake videos, includes an input device for inputting a potential DeepFake video, the input device inputs a sequence of video frames of the video, and processing circuitry. The processing circuitry detects faces frame by frame in the video to obtain consecutive face images, creates UV texture maps from the face images, inputs both face images and corresponding UV texture maps, extracts image feature maps, by a convolution neural network (CNN) backbone, from the input face images and corresponding UV texture maps and forms an input data structure, receives the input data structure, by a video transformer model that includes multiple encoders, and computes, by the video transformer model, a classification of the video as being Real or Fake. A display device plays back the potential DeepFake video and an indication that the video is Real or Fake.

A method, apparatus, and system for detecting DeepFake videos, includes an input device for inputting a potential DeepFake video, the input device inputs a sequence of video frames of the video, and processing circuitry. The processing circuitry detects faces frame by frame in the video to obtain consecutive face images, creates UV texture maps from the face images, inputs both face images and corresponding UV texture maps, extracts image feature maps, by a convolution neural network (CNN) backbone, from the input face images and corresponding UV texture maps and forms an input data structure, receives the input data structure, by a video transformer model that includes multiple encoders, and computes, by the video transformer model, a classification of the video as being Real or Fake. A display device plays back the potential DeepFake video and an indication that the video is Real or Fake.

Methods, systems, and computer readable media for controlling delivery of content are provided. In some embodiments, a system for controlling delivery of content is provided. The system includes processing circuitry configured to: transmit, to a server, a plurality of requests for blocks of the content; while at least some of the plurality of requests are still outstanding: detect a change of a service characteristic of a connection between the system and the server; determine a preferred number of outstanding requests; and cancel at least some of the requests from the plurality that are still outstanding based on the preferred number and a count of the requests from the plurality that are still outstanding.

Methods, systems, and computer readable media for controlling delivery of content are provided. In some embodiments, a system for controlling delivery of content is provided. The system includes processing circuitry configured to: transmit, to a server, a plurality of requests for blocks of the content; while at least some of the plurality of requests are still outstanding: detect a change of a service characteristic of a connection between the system and the server; determine a preferred number of outstanding requests; and cancel at least some of the requests from the plurality that are still outstanding based on the preferred number and a count of the requests from the plurality that are still outstanding.