Abstract: There has been provided a method of modifying a content stream provided to a user electronic device, the method comprising receiving device data from the user electronic device, processing the device data to determine whether one or more network conditions have been met, and modifying the content stream and/or one or more settings associated with either the content stream and or a game server. There has also provided a method of rendering a content stream provided by a sender, the method comprising, sending device data, receiving a modified content stream, and rendering the modified content stream.

Abstract: A method of providing assistance to a given user during current gameplay of a video game, the method comprises the steps of: receiving data representative of a previous gameplay, the data representative of the previous gameplay comprising video data of the previous gameplay; generating pre-processed video data by re-projecting at least part of the video data of the previous gameplay in dependence upon at least a difference between one or more first positions within the previous gameplay and one or more corresponding second positions within the current gameplay; and overlaying the pre-processed video data over the current gameplay for display to the given user.

Abstract: There is provided an image processing method for generating images including a volumetric effect. The method comprises obtaining three-dimensional “3D” volumetric effect sampling results from a simulation of a volumetric effect for a first virtual scene, rendering an image of a second virtual scene, inputting the 3D volumetric effect sampling results for the first virtual scene, and the rendered image of the second virtual scene to a neural style transfer “NST” model trained to generate an output image in dependence on a style image and a content image, and generating, by the NST model, an output image for the second virtual scene using the rendered image of the second virtual scene as the content image and the 3D volumetric effect sampling results for the first virtual scene as the style image.

Abstract: A method of identifying a predetermined event that is audibly acknowledged within content comprises the steps of obtaining at least a first reference data item representing a predetermined event, detecting a pattern of peaks in audio of the content, the peaks being detected using a predefined process and predefined criteria, comparing the pattern of peaks with a respective reference pattern in the or each reference data item using a visual comparison process, and identifying that a predetermined event has occurred within the content if the pattern of peaks matches, to a predetermined matching threshold degree, a respective reference pattern of a data item representing the predetermined event.

Abstract: A method of identifying an event that is graphically acknowledged within a sequence of images, comprises the steps of defining one or more regions of respective images in which a graphical indication of acknowledgement will occur, selecting at least a first defined region as a candidate acknowledgement image, and selecting for output one or more of the candidate acknowledgement images, or respective images of the sequence of images that comprise such candidate acknowledgement images.

Abstract: A method of identifying a predetermined event within a sequence of images comprises the steps of obtaining a database of data items each representing one of a plurality of predetermined events, identifying candidate event images within the sequence of images, comparing data representing at least a first a candidate event image with one or more data items in the database, and identifying that a predetermined event has occurred within the sequence of images if a candidate event image matches a data item in the database to a predetermined matching threshold degree.

Abstract: A method of identifying text that appears within a sequence of images comprises the steps of defining one or more regions of respective images in which text will appear, detecting when text has appeared in one or more of the defined regions, identifying text within a defined region when text has been detected there, and selecting for output identified text that has appeared, or a respective image of the sequence of images that comprise such identified text.

Abstract: A method of identifying notable events within an interactive content includes obtaining corresponding footage of at least two independent instances of the interactive content, and for the footage from each instance, identifying respective changes between successive images, that exceed a predetermined threshold, as scene cuts, and store a data item representative of an image at or near each scene cut, to create data items representing candidate events within the interactive content; then, matching or sequentially aligning the candidate events for each instance, on an overall best-match basis, retaining as review events those candidate events with a match or an aligned match between at least a majority of the candidate events, to create review events, and providing the set or series of review events to a user.

Abstract: There is provided an image processing method for generating images including a volumetric effect. The method comprises: sampling, using a 3D grid, computer-generated volumetric effect data for a virtual scene at a first sampling resolution, to obtain a first set of 3D sampling results; inputting the first set of 3D sampling results to a machine learning model trained to upscale at least part of input 3D sampling results; upscaling, by the machine learning model, at least part of the first set of 3D sampling results, to obtain a second set of 3D sampling results having a second, higher, sampling resolution; and generating one or more display images for the virtual scene at least partly in dependence on the second set of 3D sampling results.

Abstract: There is provided an image processing method for generating images including a volumetric effect. The method comprises: sampling, using a three-dimensional grid, computer-generated volumetric effect data for a virtual scene at a first sampling resolution, to obtain a first set of sampling results; upscaling at least part of the first set of sampling results by interpolating between samples of the at least part of the first set of sampling results, to obtain a second set of sampling results having a second, higher, sampling resolution; and generating one or more display images for the virtual scene at least partly in dependence on the second set of sampling results.

Abstract: A data processing apparatus rendering circuitry to render content images for a virtual environment, and image processing circuitry to generate one or more output images in response to one or more of the content images, wherein the image processing circuitry is configured to input at least one 2D volumetric effect image and one or more of the content images to a neural style transfer “NST” model, the NST model being trained to generate one or more of the output images using the at least one 2D volumetric effect image as a style image.

Abstract: A method of generating a training set for a machine learning model to upscale volumetric effect froxel grids comprises, for a source of input data for the training set, generating a low-resolution froxel grid for respective ones of a plurality of frames in sequence, the generating comprises time-averaging values contributing to the froxel grid; assigning for a given frame in the sequence the corresponding generated low-resolution froxel grid as a source of input data. For a source of target data for the training set, at the given frame in the sequence, freezing the state of a scene that is being rendered; generating a high-resolution froxel grid for repeated instances of the given frame and scene state; selecting a high resolution froxel grid generated after a predetermined number of repeated instances; and assigning the selected generated high-resolution froxel grid for the given frame as a source of target data.

Abstract: A data processing apparatus comprises sampling circuitry to sample computer-generated volumetric effect data for a virtual scene and generate an initial 2D volumetric effect image in dependence on a set of sampling results obtained for the computer-generated volumetric effect data; super resolution circuitry to generate a higher resolution 2D volumetric effect image in dependence on the initial 2D volumetric effect image, wherein the super resolution circuitry is configured to input the initial 2D volumetric effect image to a machine learning model trained for performing image super-resolution, the higher resolution 2D volumetric effect image having a higher image resolution than the initial 2D volumetric effect image; and image processing circuity to generate one or more display images for the virtual scene, wherein the image processing circuity is configured to generate one or more of the display images using the higher resolution 2D volumetric effect image.

Abstract: A method of sending a content stream to a user electronic device includes obtaining content stream data in relation to the content stream to be rendered, encoding a first portion of the content stream data, rendering a second portion of the content stream data, and sending the encoded first portion and the rendered second portion to the user electronic device. Also, a method of rendering a content stream on a user electronic device comprises receiving an encoded first portion of content stream data and a rendered second portion of the content stream data, decoding the first portion to obtain the content stream data in relation to a first portion of the content stream to be rendered, rendering the decoded first portion of the content stream data, and combining the rendered first portion of the content stream and the rendered second portion of the content stream to generate the content stream.

Abstract: There has been provided a method of modifying a content stream provided to a user electronic device, the method comprising receiving device data from the user electronic device, processing the device data to determine whether one or more network conditions have been met, and modifying the content stream and/or one or more settings associated with either the content stream and or a game server. There has also provided a method of rendering a content stream provided by a sender, the method comprising, sending device data, receiving a modified content stream, and rendering the modified content stream.

Abstract: The invention provides a computer-implemented method of detecting errors in audio outputs occurring during gameplay, the method comprising: providing an audio output distribution, the audio output distribution comprising a distribution of confirmed audio outputs that are clustered according to features of the confirmed audio outputs, wherein each of the confirmed audio outputs is a combination of audio components that occur during gameplay; receiving a sample audio output that occurs during gameplay; comparing the sample audio output to the audio output distribution; and detecting an error in the sample audio output when the sample audio output is inconsistent with the audio output distribution.

Abstract: A computer-implemented method for generating haptic feedback to accompany an audio signal, the method comprising: detecting a predetermined audio signal in one or more monitored audio channels (11); and in response to detecting the predetermined audio signal, triggering a haptic feedback signal in a haptic feedback channel (42), wherein the haptic feedback channel is synchronized with the one or more monitored audio channels.

Abstract: A method for adjusting the audio output of a video gaming system during gameplay, the method comprising: measuring the usage of a processing unit of the video gaming system during gameplay, wherein the video gaming system is configured to output an audio stream comprising a plurality of audio components, the processing unit configured to process an audio data file to output each audio component; adjusting an output quality of one or more audio components of the audio stream in response to the measured usage of the processing unit. The method allows for dynamic adjustment of the audio output quality based on the capacity of the processing unit.

Abstract: A method of mapping a virtual environment comprises obtaining a first sequence of video images output by a videogame title; obtaining a corresponding sequence of in-game virtual camera positions at which the video images were created; obtaining a corresponding sequence of depth buffer values for a depth buffer used by the videogame whilst creating the video images; and for each of a plurality of video images and corresponding depth buffer values of the obtained sequences, obtain mapping points corresponding to a sampling distribution of points over the area of a respective video image and their associated depth values; wherein respective mapping points are obtained by projecting co-ordinated derived from the sample points from the video image and associated depth values back into a 3D game world co-ordinate system of the videogame title; thereby obtaining a point cloud dataset of mapping points corresponding to the first sequence of video images.

Abstract: Computer implemented methods and systems for providing a uniform data platform for at least one application of a plurality of applications are disclosed. Exemplary methods include receiving application data from at least one of a device and a server, a device executing a portion of an application of a plurality of applications, storing the application data in a data platform, the data model including at least one metadata and at least one event, the one metadata corresponding to at least one object indicated in the data model, and the one event corresponding to a trigger condition associated with the one metadata entry, wherein values of the one metadata and one event are associated with a user profile and accessing via a plurality of remote networked services, the application data stored in the data platform, the plurality of remote network services accessing the application data using the data model.