Abstract: Systems and methods for developing improved-fidelity visual data using fidelity data and using a hierarchical algorithm are provided. An example method includes receiving at least a plurality of neighbouring sections of visual data, selecting a plurality of input sections from the received plurality of neighbouring sections of visual data, extracting features from the plurality of input sections of visual data, and producing the improved-fidelity visual data by applying the fidelity data to the extracted features.

Abstract: The present disclosure relates to encoding visual data comprising a plurality of layers using one or more hierarchical algorithms. According to an aspect, there is provided a method of encoding visual data using a plurality of layers wherein each layer encodes a different representation, and wherein one or more of the plurality of layers comprises one or more hierarchical algorithms, the method comprising the steps of: extracting one or more samples within each of the plurality of layers; and processing within each layer the one or more samples extracted in the layer; wherein in at least one of the plurality of layers the step of processing comprises applying the one or more hierarchical algorithms to the samples extracted in the layer in relation to any inter-layer prediction; and wherein the step of processing reduces a predetermined mathematical distortion between samples of a first layer and samples of a second layer.

Abstract: Methods and systems for optimising the quality of visual data. Specifically, methods and systems for preserving visual information during compression and decompression. An example method for optimising visual data includes using a pre-processing neural network to optimise visual data prior to encoding the visual data in visual data processing; and using a post-processing neural network to enhance visual data following decoding visual data in visual data processing.

Abstract: A method for developing visual data using source data, target data, and a hierarchical algorithm. According to a first aspect, there is provided a method for developing visual data from source data, target data and using a hierarchical algorithm, the method comprising the steps of: determining an alignment between the target data and the source data; and producing the visual data by transferring one or more features of the source data onto one or more features of the target data; wherein, the visual data is produced after the step of determining the alignment between the target data and the source data; and wherein the visual data is produced using the hierarchical algorithm.

Abstract: A method for developing visual data using source data, target data, and a hierarchical algorithm. According to a first aspect, there is provided a method for developing visual data from source data, target data and using a hierarchical algorithm, the method comprising the steps of: determining an alignment between the target data and the source data; and producing the visual data by transferring one or more features of the source data onto one or more features of the target data; wherein, the visual data is produced after the step of determining the alignment between the target data and the source data; and wherein the visual data is produced using the hierarchical algorithm.

Abstract: A method includes selecting a plurality of low-resolution frames associated with a video, performing a first motion estimation between a first frame and a second frame, performing a second motion estimation between a third frame and the second frame, generating a high-resolution frame representing the second frame based on the first motion estimation, the second motion estimation and the second frame using a sub-pixel convolutional neural network.

Abstract: A method for training an algorithm to process at least a section of received visual data using a training dataset and reference dataset. The method comprises an iterative method with iterations comprising: generating a set of training data using the algorithm; comparing one or more characteristics of the training data to one or more characteristics of at least a section of the reference dataset; and modifying one or more parameters of the algorithm to optimise processed visual data based on the comparison between the characteristic of the training data and the characteristic of the reference dataset. The algorithm may output the processed visual data with the same content as the at least a section of received visual data. Some aspects and/or implementations provide for improved super-resolution of lower quality images to produce super-resolution images with improved characteristics (e.g. less blur, less undesired smoothing) compared to other super-resolution techniques.

Abstract: The present disclosure relates to a method for processing input visual data using a generated algorithm based upon input visual data and the output of a calculated energy function. According to a first aspect of the disclosure, there is provided a method for enhancing input visual data using an algorithm, the method comprising evaluating gradients of the output of an energy function with respect to the input visual data; using the gradient output to enhance the input visual data; and outputting the enhanced visual data.

Abstract: Methods and systems for optimising the quality of visual data. Specifically, methods and systems for preserving visual information during compression and decompression. An example method for optimising visual data includes using a pre-processing hierarchical algorithm to optimise visual data prior to encoding the visual data in visual data processing; and using a post-processing hierarchical algorithm to enhance visual data following decoding visual data in visual data processing.

Abstract: Disclosed is method for training a plurality of visual processing algorithms for processing visual data. The method includes using a pre-processing hierarchical algorithm to process the visual data prior to encoding the visual data in visual data processing, and using a post-processing hierarchical algorithm to further process the visual data following decoding visual data in visual data processing. The encoding and decoding are performed with respect to a predetermined visual data codec and may be content specific.

Abstract: A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method including receiving at least a plurality of neighbouring sections of lower-quality visual data. A plurality of input sections from the received plurality of neighbouring sections of lower quality visual data are selected and features are extracted from those plurality of input sections of lower-quality visual data. A target section based on the extracted features from the plurality of input sections of lower-quality visual data is then enhanced.

Abstract: A system (e.g., an auto-encoder system) includes an encoder, a decoder and a learning module. The encoder generates compressed video data using a lossy compression algorithm, the lossy compression algorithm being implemented using a trained neural network with at least one convolution, generate at least one first parameter based on the compressed video data, and communicate the compressed video data and the model to at least one device configured to decode the compressed video data using an inverse algorithm based on the lossy compression algorithm. The decoder generates decoded video data based on the compressed video data using the inverse algorithm and the model, and generate at least one second parameter based on the decoded video data. The learning module trains the model using the at least one first parameter and the at least one second parameter.

Abstract: A method for enhancing lower-quality visual data using hierarchical algorithms, the method comprising the steps of: receiving one or more sections of lower-quality visual data; applying a hierarchical algorithm to the one or more sections of lower-quality visual data to enhance the one or more sections of lower-quality visual data to one or more sections of higher-quality visual data, wherein the hierarchical algorithm was developed using a learned approach; and outputting the one or more sections of higher-quality visual data.

Abstract: The present disclosure relates to analysing input data, prior to encoding, using one or more hierarchical algorithms. According to a first aspect, there is provided a method for producing output data using one or more input data and one or more hierarchical algorithms, comprising the steps of applying the hierarchical algorithm to the one or more input data; and producing output data to be used by an encoder; wherein one of the one or more input data is uncompressed; and wherein the output data is used to modify a decision making process associated with the encoder.

Abstract: A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method comprises receiving at least one section of lower-quality visual data; and extracting a subset of features, from the at least one section of lower-quality visual data. A plurality of layers of reduced-dimension visual data from the extracted features are formed and enhanced to form at least one section of higher-quality visual data. The at least one section of higher-quality visual data corresponds to the at least one section of lower-quality visual data received.

Abstract: Systems and methods generate a face-swapped image from a target image using a convolutional neural network trained to apply a source identity to the expression and pose of the target image. The convolutional neural network produces face-swapped images fast enough to transform a video stream. An example method includes aligning the face portion of a target image from an original view to a reference view to generate a target face and generating a swapped face by changing the target face to that of a source identity using a convolutional neural network trained to minimize loss of content from the target face and style from the source identity. The method also includes realigning the swapped face from the reference view to the original view and generating a swapped image by stitching the realigned swapped face with the remaining portion of the target image.

Abstract: A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method comprising receiving at least a plurality of neighbouring sections of lower-quality visual data. A plurality of input sections from the received plurality of neighbouring sections of lower quality visual data are selected and features are extracted from those plurality of input sections of lower-quality visual data. A target section based on the extracted features from the plurality of input sections of lower-quality visual data is then enhanced.

Abstract: A method for enhancing at least a section of lower-quality visual data, the method comprising at least a section of the lower-quality visual data being received. A hierarchical algorithm is then selected from a plurality of hierarchical algorithms, wherein the step of selection is based on a predetermined metric and wherein the hierarchical algorithms were developed using a learned approach and at least one of the hierarchical algorithms is operable to increase the quality of the lower-quality visual data. The selected hierarchical algorithm is then used to increase the quality of the lower-quality visual data to create a higher-quality visual data.

Abstract: A method for training learned hierarchical algorithms, the method comprising the steps of receiving input data and generating metrics from the input data. At least one hierarchical algorithm is then selected from a plurality of predetermined hierarchical algorithms based on comparing the generated metrics from the input data and like metrics for each of the plurality of predetermined hierarchical algorithms. The selected hierarchical algorithm is developed based on the input data and the developed hierarchical algorithm is outputted.

Abstract: A method for enhancing visual data when communicating visual data over a network from a first node to a second node. The method at the first node comprises developing at least one modified hierarchical algorithm from a known hierarchical algorithm operable to substantially recreate at least one section of higher-quality visual data. References to one or more known hierarchical algorithms from which the modified hierarchical algorithms were developed are transmitted to the second node along with one or more modifications to the one or more known hierarchical algorithms operable to reproduce the one or more modified hierarchical algorithms from the known hierarchical algorithms. The second node is able to recreate substantially the higher-quality video using the modified hierarchical algorithm.