Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying a variety of events that occur within a gaming session and generating event reports based on the identified events. For example, a gaming service (e.g., a cloud gaming server) can leverage content analysis and event recognizer services on a cloud computing system to detect one or more in-game events based on gaming content (e.g., video content, audio content, controller inputs) that is delivered to a client system. Systems described herein can train and implement event recognizers trained to track various in-game events across multiple gaming applications. Based on the tracked events, the systems described herein can generate event reports for events, individual users, and groups of users of the cloud computing system.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying a variety of events that occur within a gaming session and generating event reports based on the identified events. For example, a gaming service (e.g., a cloud gaming server) can leverage content analysis and event recognizer services on a cloud computing system to detect one or more in-game events based on gaming content (e.g., video content, audio content, controller inputs) that is delivered to a client system. Systems described herein can train and implement event recognizers trained to track various in-game events across multiple gaming applications. Based on the tracked events, the systems described herein can generate event reports for events, individual users, and groups of users of the cloud computing system.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying a variety of events that occur within a gaming session and implementing one or more in-game enhancements based on the identified events. For example, a gaming service (e.g., a cloud gaming server) can leverage content analysis and event recognition services on a cloud computing system to detect one or more in-game events based on gaming content (e.g., video content, audio content, controller inputs) that is delivered to a client system. Systems described herein can cause in-game enhancements to be implemented on a client system in response to detected events. Detection of events and implementation of the various enhancements may be performed without significant redevelopment of gaming applications, even where a gaming application has been previously shipped.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying a variety of events that occur within a gaming session and generating event reports based on the identified events. For example, a gaming service (e.g., a cloud gaming server) can leverage content analysis and event recognizer services on a cloud computing system to detect one or more in-game events based on gaming content (e.g., video content, audio content, controller inputs) that is delivered to a client system. Systems described herein can train and implement event recognizers trained to track various in-game events across multiple gaming applications. Based on the tracked events, the systems described herein can generate event reports for events, individual users, and groups of users of the cloud computing system.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying a variety of events that occur within a gaming session and implementing one or more in-game enhancements based on the identified events. For example, a gaming service (e.g., a cloud gaming server) can leverage content analysis and event recognition services on a cloud computing system to detect one or more in-game events based on gaming content (e.g., video content, audio content, controller inputs) that is delivered to a client system. Systems described herein can cause in-game enhancements to be implemented on a client system in response to detected events. Detection of events and implementation of the various enhancements may be performed without significant redevelopment of gaming applications, even where a gaming application has been previously shipped.

Abstract: Methods and devices for generating hardware compatible compressed textures may include accessing, at runtime of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU). The methods and devices may include converting the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model.

Abstract: The present disclosure relates to systems, methods, and computer-readable media to selectively enhance digital image and video content. For example, systems disclosed herein can encode original video content to compress and decompress the original video content. Systems described herein can further identify area of interest information for use in identifying portions of decompressed video content to analyze and remove one or more compression artifacts found therein. Systems described herein can further enhance the decompressed video content by increasing resolution for display. By identifying areas of interest and selectively enhancing digital video content, the systems described herein can reduce consumption of bandwidth and processing resources while maintaining high visual quality of the digital content.

Abstract: The present disclosure relates to systems, methods, and computer-readable media to selectively enhance digital image and video content. For example, systems disclosed herein can encode original video content to compress and decompress the original video content. Systems described herein can further identify area of interest information for use in identifying portions of decompressed video content to analyze and remove one or more compression artifacts found therein. Systems described herein can further enhance the decompressed video content by increasing resolution for display. By identifying areas of interest and selectively enhancing digital video content, the systems described herein can reduce consumption of bandwidth and processing resources while maintaining high visual quality of the digital content.

Abstract: A system and method for utilizing machine learning techniques to modify a visual quality of an area within a frame of video is provided. The method may include receiving one or more video frames of a video stream, receiving a target asset and generating, via a machine learning model, a frame mask identifying an area within the one or more video frames of the video stream that is associated with the target asset, and then modifying a visual quality of the identified area within the one or more video frames based on the frame mask. In some instances, techniques other than or in addition to machine learning techniques may be utilized. For example, template matching techniques may also be used to identify one or more areas for modifying a visual quality.

Abstract: The present disclosure relates to systems, methods, and computer-readable media to selectively enhance digital image and video content. For example, systems disclosed herein can encode original video content to compress and decompress the original video content. Systems described herein can further identify area of interest information for use in identifying portions of decompressed video content to analyze and remove one or more compression artifacts found therein. Systems described herein can further enhance the decompressed video content by increasing resolution for display. By identifying areas of interest and selectively enhancing digital video content, the systems described herein can reduce consumption of bandwidth and processing resources while maintaining high visual quality of the digital content.

Abstract: Methods and devices for real time texture compression may include accessing graphics hardware incompatible compressed textures in a format incompatible with the GPU, and a metadata file associated with the graphics hardware incompatible compressed textures, wherein the metadata file includes at least one hint that provides information to use for compression of decompressed textures from the graphics hardware incompatible compressed textures into hardware compatible compressed textures. The methods and devices may include converting the graphics hardware incompatible compressed textures into the decompressed textures. The methods and devices may include selectively compressing the decompressed textures into the hardware compatible compressed textures usable by the GPU according to the at least one hint from the metadata file. The methods and devices may include transmitting the hardware compatible compressed textures to the GPU.

Abstract: Methods and devices for generating hardware compatible compressed textures may include accessing, at runtime of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU). The methods and devices may include converting the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model.

Abstract: Methods and devices for generating hardware compatible compressed textures may include accessing, at runtime of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU). The methods and devices may include converting the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model.

Abstract: Methods and devices for real time texture compression may include accessing graphics hardware incompatible compressed textures in a format incompatible with the GPU, and a metadata file associated with the graphics hardware incompatible compressed textures, wherein the metadata file includes at least one hint that provides information to use for compression of decompressed textures from the graphics hardware incompatible compressed textures into hardware compatible compressed textures. The methods and devices may include converting the graphics hardware incompatible compressed textures into the decompressed textures. The methods and devices may include selectively compressing the decompressed textures into the hardware compatible compressed textures usable by the GPU according to the at least one hint from the metadata file. The methods and devices may include transmitting the hardware compatible compressed textures to the GPU.

Abstract: Methods and devices for generating hardware compatible compressed textures may include accessing, at runtime of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU). The methods and devices may include converting the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model.

Abstract: The subject disclosure is directed towards determining and using effort such as in a fitness/exercise/gaming environment in a way that is substantially independent of a person's height, weight, age, and gender (HWAG) properties. Effort data is recorded that represents a person's effort exerted with respect to performing at least one physical activity, in which effort data is substantially independent of weight, and possibly other HWAG properties. In one aspect, effort duration is used to allow different people to compete against others and/or against established targets/goals substantially equally, independent of a person's HWAG properties.