Abstract: As understood herein, not all objects in a computer game are of equal priority. Given this recognition, present principles apply a range of spatial and temporal resolutions to various objects in the same game depending on the priority of the objects in the game.

Abstract: A machine learning (ML) model is trained using pairs of images. Each pair includes an image of a human face and a duplicate of the image with a computer game headset overlaid on the face using computer graphics. The ML model subsequently can be used to receive an image of a gamer wearing a headset and output a full-face image of the gamer for use in, e.g., social network settings related to the game.

Abstract: Techniques are described for over-training a ML model on multiple gameplay videos of individual scenes of a computer game to better configure the model to reconstruct or enhance portions of the computer game at a receiver as the computer game is received over a streamlining network. Reconstruction of individual missing slices of a frame is contemplated such that a frame missing a slice need not be entirely discarded.

Abstract: Techniques are described for reducing latency in networked gaming by reducing I-frame sizes (which also results in automatically increasing P-frame sizes) to reduce the overall amount of video being transmitted. The reduced size of the I-frames is compensated for by increasing the size of other frames using a low pass filter (LPF) such as a Gaussian filter which reduces sharpness that the decoder can try to recover, or by use of lower resolution. The I-frame can be reduced by rotating it or flipping/mirroring it to produce the smaller coded frame, sending a flag to signal the orientation.

Abstract: Techniques are described for determining if chat between two computer gamers requiring translation is related to the game or is simple social chat. Depending on the type of chat, a machine learning (ML) model is selected to predict completion of the sentence being input by the speaker to reduce latency in translating and transmitting the chat.

Abstract: Techniques are described for translating computer game chat to a different language for a partner gamer. If the chat is determined to be related to a game being played as opposed to social chat, a glossary may be used tailored to that game to first determine if a spoken term is in the glossary. If it is, a lookup of an equivalent term from the glossary can be used instead of passing the term through a translation engine, increasing accuracy and saving translation time.

Abstract: Techniques are described for an encoder or decoder to adaptively change coding based on a specific user's sensitivity to flickering, or flashing, or blockiness, or other visual artifacts. Alternatively, video may be pre-processed based on the user's sensitivity to suppress artifacts prior to encoding and/or post-processed to suppress artifacts after decoding.

Abstract: An image of a computer game player wearing a headset that occludes part of the face is input to a trained machine learning (ML) model, which outputs in response a full-face image that is not occluded for use in, e.g., social network settings related to the game.

Abstract: A machine learning (ML) model is trained using pairs of images. Each pair includes an image of a human face and a duplicate of the image with a computer game headset overlaid on the face using computer graphics. The ML model subsequently can be used to receive an image of a gamer wearing a headset and output a full-face image of the gamer for use in, e.g., social network settings related to the game.

Abstract: Video stream data is selectively scaled so that sections within regions of interest (ROI) maintain high resolution while areas not within the region of interest are down-scaled to reduce bandwidth cost of transmission. A low compression encoder compresses sections of a video frame corresponding to one or more ROI without motion search or prediction mode decision to generate low-compression section data. The video frame is downscaled and a high compression encoder compresses the resulting downscaled video frame with prediction mode decision to generate high-compression frame data.

Abstract: Video stream data is selectively scaled so that sections within regions of interest (ROI) maintain high resolution while areas not within the region of interest are down-scaled to reduce bandwidth cost of transmission. A low compression encoder compresses sections of a video frame corresponding to one or more ROI without motion search or prediction mode decision to generate low-compression section data. The video frame is downscaled and a high compression encoder compresses the resulting downscaled video frame with prediction mode decision to generate high-compression frame data.

Abstract: Gaze tracking data is analyzed to determine one or more regions of interest within an image of a video stream. The video stream data is selectively scaled so that sections within the regions of interest maintain high resolution while areas not within the region of interest are down-scaled to reduce bandwidth cost of transmission. A scheme for reduction of motion sickness by reducing the size of the high resolution area is also claimed.

Abstract: Gaze tracking data is analyzed to determine one or more regions of interest within an image of a video stream. The video stream data is selectively scaled so that sections within the regions of interest maintain high resolution while areas not within the region of interest are down-scaled to reduce bandwidth cost of transmission. A scheme for reduction of motion sickness by reducing the size of the high resolution area is also claimed.