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: The present disclosure relates to an enhanced in-loop filter for an encoding or decoding process. According to an aspect of the disclosure, there is provided method of post filtering video data in an encoding or decoding process using hierarchical algorithms, the method comprising steps of: receiving one or more input pictures of video data; transforming, using one or more hierarchical algorithms, the one or more input pictures of video data to one or more pictures of transformed video data; and outputting the one or more transformed pictures of video data; wherein the transformed pictures of video data are enhanced for use within the encoding or decoding loop and wherein the method is performed in-loop within the encoding or decoding process.

Abstract: Generating texture maps for use in rendering visual output. According to a first aspect, there is provided a method for generating textures for use in rendering visual output, the method comprising the steps of: generating, using a first hierarchical algorithm, a first texture from one or more sets of initialisation data; and selectively refining the first texture, using one or more further hierarchical algorithms, to generate one or more further textures from at least a section of the first texture and one or more sets of further initialisation data; wherein at least a section of each of the one or more further textures differs from the first texture.

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: The present disclosure relates to the use of hierarchical algorithms to temporally interpolate enhanced reference pictures for use in video encoding and decoding. According to a first aspect, there is provided a method of generating enhanced reference pictures in a video encoding and/or decoding process, the method comprising: receiving one or more known reference elements of video data from a reference picture buffer; generating, using one or more hierarchical algorithms, one or more additional reference elements of video data from the one or more known reference elements of video data; and outputting the one or more additional reference elements of video data; wherein the generating the one or more additional reference elements of video data from the one or more known reference elements of video data comprises the use of temporal interpolation.

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: 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: 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: Use of machine learning to improve motion estimation in video encoding. According to a first aspect, there is provided a method for estimating the motion between pictures of video data using a hierarchical algorithm, the method comprising steps of: receiving one or more input pictures of video data; identifying, using a hierarchical algorithm, one or more reference elements in one or more reference pictures of video data that are similar to one or more input elements in the one or more input pictures of video data; determining an estimated motion vector relating the identified one or more reference elements to the one or more input elements; and outputting an estimated motion vector.

Abstract: Use of machine learning to improve motion compensation in video encoding. According to a first aspect, there is provided a method for motion compensation in video data using hierarchical algorithms, the method comprising the steps of: receiving one or more original blocks of video data and one or more reference blocks of video data; determining, using one or more hierarchical algorithms, one or more predicted blocks of video data from the one or more reference blocks of video data; and calculating one or more residual blocks of video data from the one or more predicted blocks of video data and the one or more original blocks of video data.

Abstract: A neural network is trained to process received visual data to estimate a high-resolution version of the visual data using a training dataset and reference dataset. A set of training data is generated and a generator convolutional neural network parameterized by first weights and biases is trained by comparing characteristics of the training data to characteristics of the reference dataset. The first network is trained to generate super-resolved image data from low-resolution image data and the training includes modifying first weights and biases to optimize processed visual data based on the comparison between the characteristics of the training data and the characteristics of the reference dataset.

Abstract: A method for increasing the quality of a section of visual data communicated over a network from a first node to a second node, the method at the second node including receiving a lower-quality visual data via a network, receiving a corresponding reference to an algorithm operable to increase a quality of the lower-quality visual data, the algorithm selected based on a higher-quality visual data from which the lower-quality visual data was generated, and using the algorithm to increase the quality of the lower-quality visual data to recreate the higher-quality visual data.

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 developing an enhancement model for low-quality visual data, the method comprising the steps of receiving one or more sections of higher-quality visual data; and training a hierarchical algorithm. The hierarchical algorithm is operable to increase the quality of one or more sections of lower-quality visual data so as to substantially reproduce the one or more sections of higher-quality visual data. The hierarchical algorithm is then outputted.

Abstract: A method for enhancing one or more sections of lower-quality visual data using a hierarchical algorithm, the method comprising receiving one or more sections of lower-quality visual data. The one or more sections of lower-quality visual data are enhanced to one or more sections of higher-quality visual data using the hierarchical algorithm. Additionally, at least the first step of the hierarchical algorithm is performed in a lower-quality domain; and wherein the hierarchical algorithm operates in both a higher-quality domain and the lower-quality domain.

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.