Abstract: Provided is an artificial intelligence (AI) encoding apparatus including a memory storing one or more instructions, and a processor configured to execute the one or more instructions stored in the memory to obtain a first image by performing AI down-scaling on an original image through a deep neural network (DNN) for down-scaling, obtain artifact information indicating an artifact region in the first image, perform post-processing to change a pixel value of a pixel in the first image, based on the artifact information, and obtain image data corresponding to a result of encoding of the post-processed first image, and AI data including the artifact information.

Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to

Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to

Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to

Abstract: Provided is a decoding apparatus including: a communication interface configured to receive AI encoding data generated as a result of artificial intelligence (AI) down-scaling and first encoding of an original image; a processor configured to divide the AI encoding data into image data and AI data; and an input/output (I/O) device, wherein the processor is further configured to: obtain a second image by performing first decoding on a first image obtained by performing AI down-scaling on the original image, based on the image data; and control the I/O device to transmit the second image and the AI data to an external apparatus. In some embodiments, the external apparatus performs an AI upscaling of the second image using the AI data, and displays the resulting third image.

Abstract: A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.

Abstract: An artificial intelligence (AI) decoding apparatus includes a memory storing one or more instructions, and a processor configured to execute the stored one or more instructions, to obtain image data corresponding to a first image that is encoded, obtain a second image corresponding to the first image by decoding the obtained image data, determine whether to perform AI up-scaling of the obtained second image, based on the AI up-scaling of the obtained second image being determined to be performed, obtain a third image by performing the AI up-scaling of the obtained second image through an up-scaling deep neural network (DNN), and output the obtained third image, and based on the AI up-scaling of the obtained second image being determined to be not performed, output the obtained second image.

Abstract: A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.

Abstract: An artificial intelligence (AI) decoding apparatus includes a memory storing one or more instructions, and a processor configured to execute the stored one or more instructions, to obtain image data corresponding to a first image that is encoded, obtain a second image corresponding to the first image by decoding the obtained image data, determine whether to perform AI up-scaling of the obtained second image, based on the AI up-scaling of the obtained second image being determined to be performed, obtain a third image by performing the AI up-scaling of the obtained second image through an up-scaling deep neural network (DNN), and output the obtained third image, and based on the AI up-scaling of the obtained second image being determined to be not performed, output the obtained second image.

Abstract: A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.

Abstract: Provided is an image processing apparatus that detects a predetermined image region of an object from a photographed image; generates a correction parameter; and corrects a high-luminance region by using a correction parameter, in which, in a case where the high-luminance region is in an image region having a predetermined image region of a plurality of objects overlapping with each other therein, to which object having the predetermined image region the high-luminance region belongs is judged, and, on the basis of information of the predetermined image region the high-luminance region belongs to, the correction parameter with which the high-luminance region in the overlapping image region is corrected is generated.

Abstract: According to an embodiment, an image analysis device includes one or more processors. The one or more processors configured to: calculate a feature map of a target image; calculate context score information representing context of each pixel in the target image on the basis of the feature map; calculate shape score information representing a shape of an object in at least one region included in the target image on the basis of the feature map; correct the shape score information in the at least one region using the context score information in a corresponding region; and output the corrected shape score information in the at least one region.

Abstract: Disclosed is an apparatus comprising a processing device and a method for color correction of an image in a processing device, the method comprising: obtaining an image; determining a Laplacian matrix of the image; obtaining a first region of the image, the first region being indicative of a part of the image to be color corrected; obtaining a second region of the image; obtaining a first recoloring image based on the second region; determining a first corrected region of a first corrected image based on the Laplacian matrix and the first recoloring image; and obtaining and outputting a corrected image based on the first corrected region of the first corrected image.

Abstract: The present disclosure generally relates to a method for predicting at least one block of pixels of a view (170) belonging to a matrix of views (17) obtained from light-field data belong with a scene. According to present disclosure, the method is implemented by a processor and comprises for at least one pixel to predict of said block of pixels: —from said matrix of views (17), obtaining (51) at least one epipolar plane image (EPI) belong with said pixel to predict, —among a set of unidirectional prediction modes, determining (52) at least one optimal unidirectional prediction mode from a set of previous reconstructed pixels neighboring said pixel to predict in said at least one epipolar plane image, —extrapolating (53) a prediction value of said pixel to predict by using said at least one optimal unidirectional prediction mode.

Abstract: Systems and methods identify consumer products in images. Known consumer products are captured as grayscale or color images. They are converted to binary at varying thresholds. Connected components in the binary images identify image features according to pixels of a predetermined size, shape, solidity, aspect ratio, and the like. The image features are stored and searched for amongst image features similarly extracted from unknown images of consumer products. Identifying correspondence between the features of the images lends itself to identifying or not known consumer products.

Abstract: A method, an image processing device, and a system for generating a depth map are proposed. The method includes the following steps. A first original image and a second original image are obtained, and first edge blocks corresponding to the first original image and second edge blocks corresponding to the second original image are obtained. Depth information of edge blocks is generated according to the first edge blocks and the second edge blocks, and depth information of non-edge blocks is set according to the depth information of the edge blocks. The depth map is generated by using the depth information of the edge blocks and the depth information of the non-edge blocks.

Abstract: Methods and systems for reducing an amount of data storage and/or processing power necessary for training data for machine learning by replacing three dimensional models with a plurality of two dimensional images are disclosed. The method includes determining an object of interest from a three-dimensional model and cropping the object of interest into a plurality of two-dimensional images. The plurality of two-dimensional images are cropped such that only the object of interest remain. The plurality of two-dimensional images are cropped with respect to particular attributes, such as a road width, a road angle, or an angle with respect to an adjacent vehicle. An image capturing device captures the real time background images. The objects within the background images are categorized using associated attributes. The attributes are synthesized with the plurality of two-dimensional images such that a plurality of replica two-dimensional images of the 3D model with real-time backgrounds are generated.

Abstract: A method and an apparatus for detecting a road lane are provided. The method includes acquiring a current road image of a road around a vehicle and inputting the current road image into a deep learning model and detecting a road lane region in the current road image based on a result outputted from the deep learning model. The deep learning model includes a first model device and a second model device. The first model device includes at least one first model subdevice which includes a convolutional neural network and a first recurrent neural network, and the second model device includes at least one second model subdevice which includes a deconvolution neural network and a second recurrent neural network.

Abstract: A method of performing automated object inspection includes obtaining a plurality of test images. For each of the plurality of test images, the method includes quantitatively determining a respective transformation from a predefined contour of the reference marker in the predefined common layout to a respective anchor contour corresponding to the reference marker as captured in the test image; and applying an inverse of the respective transformation to at least a portion of the test image to obtain a respective regularized version of the test image, such that the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation. The method further includes performing independent automated inspection on each of two or more sub-portions of the respective regularized versions of the test images.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for improving quality of location-related content, such as truncated location-related content, for example, while preserving and/or maintaining user privacy.