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 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: An example system includes a processor and a memory. The system performs sub-pixel convolution that is free of checkerboard artifacts. In one example implementation, the system may execute a method that includes initializing one or more parameters of a sub-kernel of a kernel and copying the one or more parameters of the sub-kernel to other sub-kernels of the kernel. The method may further include performing convolution of an input image with the sub-kernels of the kernel and generating a plurality of first output images. A second output image is then generated based on the plurality of first output images.

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 includes selecting two or more frames from a plurality of frames of a video, downscaling the two or more frames, estimating a flow data based on an optical flow associated with the downscaled two or more frames, upscaling the flow data, generating a refined flow data based on the upscaled flow data and the downscaled two or more frames, upscaling the refined flow data, and synthesizing an image based on the upscaled refined flow data and the two or more frames.

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 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 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: 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: 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 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: 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 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 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 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.