Abstract: This specification relates to methods of compression that utilise machine-learning. According to a first aspect of this specification, there is described a computer implemented method comprising: receiving, at a decoder and via a network, a first encoded representation of a set of input data and processing, by the decoder, the first encoded representation using a machine-learning decoder model to generate a first set of decoded data corresponding to the set of input data. The method further comprises processing, at the decoder, the first set of decoded data using a machine-learning encoder model to generate a second encoded representation of the set of input data. The machine-learning encoder model corresponds to an encoder-side machine-learning encoder model that was used to generate the first encoded representation. The method further comprises processing, at the decoder, the second encoded representation using the machine-learning decoder model to generate a second set of decoded data.

Abstract: This specification relates to methods of compression that utilise machine-learning. According to a first aspect of this specification, there is described a computer implemented method comprising: receiving, at a decoder and via a network, a first encoded representation of a set of input data and processing, by the decoder, the first encoded representation using a machine-learning decoder model to generate a first set of decoded data corresponding to the set of input data. The method further comprises processing, at the decoder, the first set of decoded data using a machine-learning encoder model to generate a second encoded representation of the set of input data. The machine-learning encoder model corresponds to an encoder-side machine-learning encoder model that was used to generate the first encoded representation. The method further comprises processing, at the decoder, the second encoded representation using the machine-learning decoder model to generate a second set of decoded data.

Abstract: Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

Abstract: Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

Abstract: Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhacement. In at least one embodiment, a noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.