A system comprises a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory. A communication component receives a stream of medical images from a medical image data source via a network for displaying in one or more viewports of a medical image visualization application that provides interactive functionalities in association with the displaying. A buffer component stores the medical images, in one or more buffers, as the respective medical images are received. A monitoring component monitors user activity with respect to a rendered subset of the medical images in the one or more viewports in association with usage of the interactive functionalities. A buffer management component regulates storing of the medical images in the one or more buffers by the buffer component as a function of the user activity to facilitate seamless rendering of the medical images.

A system comprises a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory. A communication component receives a stream of medical images from a medical image data source via a network for displaying in one or more viewports of a medical image visualization application that provides interactive functionalities in association with the displaying. A buffer component stores the medical images, in one or more buffers, as the respective medical images are received. A monitoring component monitors user activity with respect to a rendered subset of the medical images in the one or more viewports in association with usage of the interactive functionalities. A buffer management component regulates storing of the medical images in the one or more buffers by the buffer component as a function of the user activity to facilitate seamless rendering of the medical images.

An electronic apparatus connected to an image capture apparatus receives an image capture instruction for requesting the image capture apparatus to perform image capture processing, in response to a user operation. If the image capture instruction is received, the electronic apparatus performs control to display a first image recorded by the image capture apparatus performing the image capture processing according to a capture request based on the image capture instruction from the electronic apparatus on a screen without a second image recorded by the image capture apparatus automatically performing the image capture processing according to satisfying a predetermined condition for automatic capturing. If the image capture instruction is not received, the electronic apparatus performs control to display both the first image and the second image on the screen.

An electronic apparatus connected to an image capture apparatus receives an image capture instruction for requesting the image capture apparatus to perform image capture processing, in response to a user operation. If the image capture instruction is received, the electronic apparatus performs control to display a first image recorded by the image capture apparatus performing the image capture processing according to a capture request based on the image capture instruction from the electronic apparatus on a screen without a second image recorded by the image capture apparatus automatically performing the image capture processing according to satisfying a predetermined condition for automatic capturing. If the image capture instruction is not received, the electronic apparatus performs control to display both the first image and the second image on the screen.

Concepts and technologies disclosed herein are directed to pictograms as digitally recognizable tangible controls. According to one aspect disclosed herein, a user system can include a processing component and a memory component. The memory component can include instructions of a pictogram digitization module. The user system can capture, via a camera component, an image containing a pictogram that is a digitally recognizable tangible manifestation of a digital control. The user system can determine, via the pictogram digitization module, the digital control associated with the pictogram. The user system can implement, via the pictogram digitization module, the digital control. The digital control can include a digital content, an action, or a context. The user system can create, via the pictogram digitization module, a digital interface that includes the digital control. In some embodiments, the pictogram includes a formal pictogram. In other embodiments, the pictogram includes an informal pictogram.

Concepts and technologies disclosed herein are directed to pictograms as digitally recognizable tangible controls. According to one aspect disclosed herein, a user system can include a processing component and a memory component. The memory component can include instructions of a pictogram digitization module. The user system can capture, via a camera component, an image containing a pictogram that is a digitally recognizable tangible manifestation of a digital control. The user system can determine, via the pictogram digitization module, the digital control associated with the pictogram. The user system can implement, via the pictogram digitization module, the digital control. The digital control can include a digital content, an action, or a context. The user system can create, via the pictogram digitization module, a digital interface that includes the digital control. In some embodiments, the pictogram includes a formal pictogram. In other embodiments, the pictogram includes an informal pictogram.

Systems and methods provide a learned difference metric that operates in a wide artifact space. An example method includes initializing a committee of deep neural networks with labeled distortion pairs, iteratively actively learning a difference metric using the committee and psychophysics tasks for informative distortion pairs, and using the difference metric as an objective function in a machine-learned digital file processing task. Iteratively actively learning the difference metric can include providing an unlabeled distortion pair as input to each of the deep neural networks in the committee, a distortion pair being a base image and a distorted image resulting from application of an artifact applied to the base image, obtaining a plurality of difference metric scores for the unlabeled distortion pair from the deep neural networks, and identifying the unlabeled distortion pair as an informative distortion pair when the difference metric scores satisfy a diversity metric.

Systems and methods provide a learned difference metric that operates in a wide artifact space. An example method includes initializing a committee of deep neural networks with labeled distortion pairs, iteratively actively learning a difference metric using the committee and psychophysics tasks for informative distortion pairs, and using the difference metric as an objective function in a machine-learned digital file processing task. Iteratively actively learning the difference metric can include providing an unlabeled distortion pair as input to each of the deep neural networks in the committee, a distortion pair being a base image and a distorted image resulting from application of an artifact applied to the base image, obtaining a plurality of difference metric scores for the unlabeled distortion pair from the deep neural networks, and identifying the unlabeled distortion pair as an informative distortion pair when the difference metric scores satisfy a diversity metric.

An online system presents a content item to users and receives selections of reaction icons from the users. The online system generates a background animation with the selected reaction icons and a foreground animation to be layered on top of the background animation. The online system sends the background and foreground animations to a client device to be cached. Further, the online system presents the content item to a viewing user associated with the client device and receives a selection of a reaction icon from the viewing user. The online system selects a subset of the users based on the viewing user's affinity to the users, retrieves images of the selected users, and send the images to the client device. The client device customizes the background and foreground animations based on the images and the viewing user's reaction icon to generate a compound animation for display to the viewing user.

An online system presents a content item to users and receives selections of reaction icons from the users. The online system generates a background animation with the selected reaction icons and a foreground animation to be layered on top of the background animation. The online system sends the background and foreground animations to a client device to be cached. Further, the online system presents the content item to a viewing user associated with the client device and receives a selection of a reaction icon from the viewing user. The online system selects a subset of the users based on the viewing user's affinity to the users, retrieves images of the selected users, and send the images to the client device. The client device customizes the background and foreground animations based on the images and the viewing user's reaction icon to generate a compound animation for display to the viewing user.