Disclosed are systems, methods, and non-transitory computer-readable media for adjusting depth of AR content on HUD. A viewing device identifies, based on sensor data, a physical object visible through a transparent display of the vehicle. The sensor data indicates an initial distance of the physical object from the vehicle. The viewing device gathers virtual content corresponding to the physical object and generates an initial presentation of the virtual content based on the initial distance. The viewing device presents the initial presentation of the virtual content on the transparent display at a position on the transparent display corresponding to the physical object. The viewing device determines, based on updated sensor data, an updated distance of the physical object and generates an updated presentation of the virtual content based on the updated distance. The viewing device presents the updated presentation of the virtual content on the transparent display of the vehicle.

The present invention relates to a system and a method of preloading a multi-view video. According to an embodiment of the present invention, load of a system is reduced and client's Quality of Experience (QoE) is maximized. In addition, the sense of direction, distance, and space for virtual reality are felt in the same manner as those for the real environment, so that a virtual reality service is provided realistically, thereby further improving immersion and interest in the virtual reality service.

A medical image processing device includes: a memory; and a processor including hardware. The processor is configured to: generate, by performing enlargement processing or shrinking processing to first observation image information input from an outside, second observation image information having number of pixels different from predetermined number of pixels, the first observation image information being generated by capturing a subject and having the predetermined number of pixels; generate third observation image information by performing enhancement processing for enhancing a structure of the subject to the second observation image information, the structure of the subject being contained in a second observation image corresponding to the second observation image information; and generate and output fourth observation image information having different number of pixels from that of the second observation image information by performing enlargement processing or shrinking processing to the third observation image information.

Artistic styles extracted from source images may be applied to target images to generate stylized images and/or video sequences. The extracted artistic styles may be stored as a plurality of layers in one or more neural networks, which neural networks may be further optimized, e.g., via the fusion of various elements of the networks' architectures. The artistic style may be applied to the target images and/or video sequences using various optimization methods, such as the use of a first version of the neural network by a first processing device at a first resolution to generate one or more sets of parameters (e.g., scaling and/or biasing parameters), which parameters may then be mapped for use by a second version of the neural network by a second processing device at a second resolution. Analogous multi-processing device and/or multi-network solutions may also be applied to other complex image processing tasks for increased efficiency.

In one embodiment, a method includes receiving a first frame associated with a first time and one or more second frames of a video having a resolution lower than a target resolution, wherein each second frame is associated with a second time prior to the first time, generating a first feature map for the first frame and one or more second feature maps for the one or more second frames, up-sampling the first feature map and the one or more second feature maps to the target resolution, warping each of the up-sampled second feature maps according to a motion estimation between the associated second time and the first time, and generating a reconstructed frame having the target resolution corresponding to the first frame by using a machine-learning model to process the up-sampled first feature map and the one or more up-sampled and warped second feature maps.

Systems and methods are provided for obtaining and managing remote sensing data (e.g. Earth observation data). A remote sensing platform obtains imagery and other remote sensing data of the Earth and other planetary objects. The remote sensing platform includes the International Space Station, or manned and unmanned spacecraft or aircraft. A sensor captures observation data and transmits the data to ground stations on the Earth. A ground segment receives and stores the data. Users use an order management system to place orders for the observation data, which specify processing parameters for the remote sensing data. The remote sensing data is retrieved from storage is processed according to the parameters to generate a data product. This system provides tools for searching and analyzing the data, and for interacting with the system through an API. The system combines data that is produced by the remote sensing platform and by third parties.

In one example, a method performed by a processing system including at least one processor includes receiving an extended reality stream from a remote server over a network connection, presenting an extended reality experience to a user endpoint device by playing back the extended reality stream, measuring a latency of the network connection between the processing system and the remote server, and displaying a visual indicator of the latency that was measured on a display of the user endpoint device.

The present invention discloses a method and system for directed transfer of cross-domain data based on high-resolution remote sensing images. In the method of the present invention, first, an objective loss function which combines an image translation loss and a model adaptive loss of an image translation network model is established, thus overcoming the technical shortcoming that an existing data translation technique fails to take a specific task into full consideration and ignores a negative impact of data translation on the specific task. Further, a trained image translation network model is fine-tuned based on sample data, so that the image translation network model keeps translation towards the effect desired by the target model, thus avoiding over-interpretation or over-simplification during directed transfer of cross-domain data and improving accuracy of directed transfer of the cross-domain data based on the high-resolution remote sensing images.

One embodiment of the present invention sets forth a technique for performing remastering of video content. The technique includes determining a first input frame corresponding to a first frame included in a first video and a first target frame corresponding to a second frame included in a second video based on one or more alignments between the first frame and the second frame. The technique also includes executing a machine learning model to convert the first input frame into a first output frame. The technique further includes training the machine learning model based on one or more losses associated with the first output frame and the first target frame.

A high-definition image is preprocessed to generate a substantially losslessly-reconstructable set of image components that include a relatively low-resolution base image and a plurality of extra-data images that provide for progressively substantially losslessly reconstructing the high-definition image from the base image, wherein a single primary-color component of the extra-data images provides for relatively quickly reconstructing full-resolution intermediate images during the substantially lossless-reconstruction process.