A method in an illustrative embodiment includes dividing a first image having a first resolution into a plurality of image patches, and converting each of the plurality of image patches into an image patch having a second resolution by using a plurality of candidate conversion models, the second resolution being higher than the first resolution. The method may include determining a plurality of quality factors corresponding to the plurality of candidate conversion models for each of the converted image patches. The method may include selecting a conversion model for each image patch from the plurality of candidate conversion models based on the plurality of quality factors and computation load factors of the plurality of candidate conversion models, and generating metadata for converting the first image into a second image having the second resolution based on position information of each image patch and the conversion model selected for each image patch.

Disclosed are a face image and iris image acquisition method and device, a computer-readable readable storage medium and an apparatus. The method includes rotating the first tripod head to force the face lens and the iris lens to be in acquisition positions; capturing a first face image and a first iris image simultaneously by the face lens and the iris lens; and locating the iris in the first iris image, and if no iris is located, determining whether a condition of light-avoiding rotation is satisfied, and if the condition is satisfied, rotating the second tripod head to adjust an angle or a position of the supplementary light source to enable a light spot region to avoid an iris region.

A system for non-reference video-quality prediction includes a video-processing block to receive an input bitstream and to generate a first vector, and a neural network to provide a predicted-quality vector after being trained using training data. The training data includes the first vector and a second vector, and elements of the first vector include high-level features extracted from a high-level syntax processing of the input bitstream.

A device includes a memory and one or more processors. The memory is configured to store an image enhancement network of an image enhancer. The one or more processors are configured to predict an image compression quality of an image of a stream of images. The one or more processors are also configured to configure the image enhancer based on the image compression quality. The one or more processors are further configured to process, using the image enhancement network of the configured image enhancer, the image to generate an enhanced image.

A computer-implemented method for processing an electronic image may include receiving, by an artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital whole slide images (WSIs) and extracting one or more vectors of features from one or more foreground tiles of tile images of the one or more digital WSIs. The method may include running a trained machine learning model on the one or more vectors of features and determining, based on an output of the trained machine learning model, whether one or more quality issues are present in the one or more digital WSIs.

Stitching of multiple images into a composite representation can be performed using a set of stitching parameters determined based, at least in part, upon a subjective stitching quality assessment value. A stitched image can be compared against its constituent images to obtain one or more objective quality metrics. These objective quality metrics can be fed, as input, to a trained classifier, which can infer a subjective quality assessment metric for the stitched (or otherwise composited) image. This subjective quality assessment metric can be used to adjust one or more compositing parameter values in order to provide at least a minimum subjective quality assessment value for composited images.

Aspects of the disclosure provide methods and apparatuses of quality assessment for three dimensional (3D) graphics modeling. In some examples, an apparatus for quality assessment includes processing circuitry. The processing circuitry determines an updated virtual camera position in response to a position change of a virtual camera for projection from a three-dimensional (3D) space to a two-dimensional (2D) space. The processing circuitry projects a reference 3D representation according to the updated virtual camera position to generate a reference 2D image, and projects a 3D representation under assessment according to the updated virtual camera position to generate an assessment 2D image. The processing circuitry calculates an assessment score associated with the updated virtual camera position based on the reference 2D image and the assessment 2D image.

Disclosed are embodiments of a graphics scene detection technique that provides an adaptive scale factor dependent on an image quality, such that certain images may be downscaled prior to being displayed to preserve system resources without significantly affecting image quality for a user. The inventors recognized and appreciated that certain images may be presented at a lower resolution to a user without being perceived as lower image quality. Some aspects provide a scene detection module that determines a quality score from a graphic command output for an image. Depending on the quality score, the scene detection module may output a quality-aware scale factor that can be applied to reduce pixel resolution of an image before displaying the image to a user. Resultingly, the computing device may be improved by saving system resources including memory bandwidth, processing power for other apps or instances, without negatively affecting visual perception of the scene.

A system and method for reviewing a tomosynthesis image data set comprising volumetric image data of a breast, the method comprising, in one embodiment, causing an image or a series of images from the data set to be displayed on a display monitor and selecting or indicating through a user interface an object or region of interest in a presently displayed image of the data set, thereby causing an image from the data set having a best focus measure of the user selected or indicated object or region of interest to be automatically displayed on the display monitor.

This image quality evaluation device comprises: a conversion unit that converts the data of an image to two-dimensional array data of the luminance value of each pixel in the image; a processing unit that executes an averaging process, based on a plurality of filter sizes, on each pixel of the two-dimensional array data; and an evaluation unit that evaluates the quality of the image using the processing results of the processing unit.