Abstract: An image processing apparatus may include: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor executes the one or more instructions to use one or more convolutional neural networks to: determine first parameters, based on position information of first pixels included in a first region of a first image and position information of samples of the first image; obtain sample values of the samples by applying the first parameters to pixel values of the first pixels; obtain feature information corresponding to the first region by performing a convolution operation between the sample values and a kernel of the one or more convolutional neural networks; and generate a second image, based on the feature information.

Abstract: Methods and apparatuses for encoding and decoding an intra prediction mode of a prediction unit of a chrominance component based on an intra prediction mode of a prediction unit of a luminance component are provided. When an intra prediction mode of a prediction unit of a luminance component is the same as an intra prediction mode in an intra prediction mode candidate group of a prediction unit of a chrominance component, reconstructing the intra prediction mode candidate group of the prediction unit of the chrominance component by excluding or replacing an intra prediction mode of the prediction unit of the chrominance component which is same as an intra prediction mode of the prediction unit of the luminance component from the intra prediction mode candidate group, and encoding the intra prediction mode of the prediction unit of the chrominance component by using the reconstructed intra prediction mode candidate group.

Abstract: Provided are methods and apparatus for encoding and decoding motion information. The method of encoding motion information includes: obtaining a motion information candidate by using motion information of prediction units that are temporally or spatially related to a current prediction unit; adding, when the number of motion information included in the motion information candidate is smaller than a predetermined number n, alternative motion information to the motion information candidate so that the number of motion information included in the motion information candidate reaches the predetermined number n; determining motion information with respect to the current prediction unit from among the n motion information candidates; and encoding index information indicating the determined motion information as motion information of the current prediction unit.

Abstract: An apparatus for processing audio includes at least one processor configured to obtain a down-mixed audio signal from a bitstream, to obtain down-mixing-related information from the bitstream, to de-mix the down-mixing-related information by using down-mixing-related information, and to reconstruct an audio signal including at least one frame based on the de-mixed audio signal. The down-mixing-related information is information generated in units of frames by using an audio scene type.

Abstract: An image processing device includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: obtain location change feature information based on values of pixels included in a first image and relative location relationships of the pixels included in the first image and pixels included in a second image; and generate the second image obtained by up-scaling the first image by k times, by performing a convolution operation on the location change feature information and a kernel, wherein k is a real number.

Abstract: An image processing apparatus, including a memory configured to store one or more instructions; and at least one processor configured to execute the one or more instructions to: based on a first image and a probability model, optimize an estimated pixel value and estimated gradient values of each pixel of an original image corresponding to the first image, obtain an estimated original image based on the optimized estimated pixel value of the each pixel of the original image, obtain a decontour map based on the optimized estimated pixel value and the estimated gradient values of the each pixel of the original image, and generate a second image by combining the first image with the estimated original image based on the decontour map.

Abstract: Methods and apparatuses for encoding and decoding an intra prediction mode of a prediction unit of a chrominance component based on an intra prediction mode of a prediction unit of a luminance component are provided. When an intra prediction mode of a prediction unit of a luminance component is the same as an intra prediction mode in an intra prediction mode candidate group of a prediction unit of a chrominance component, reconstructing the intra prediction mode candidate group of the prediction unit of the chrominance component by excluding or replacing an intra prediction mode of the prediction unit of the chrominance component which is same as an intra prediction mode of the prediction unit of the luminance component from the intra prediction mode candidate group, and encoding the intra prediction mode of the prediction unit of the chrominance component by using the reconstructed intra prediction mode candidate group.

Abstract: An image processing device, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: obtain kernel coefficient information corresponding to each pixel of a plurality of pixels included in a first image, using a convolution neural network including one or more convolution layers, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on a gradient kernel set including a plurality of gradient kernels corresponding to one or more gradient characteristics of the plurality of pixels, and the kernel coefficient information, and generate a second image, by applying the kernel included in the spatially variant kernel to a region centered on the each pixel, and filtering the first image.

Abstract: An image processing apparatus and a method of operating the same are provided. The method includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to obtain first frequency coefficient information by converting a first image into a frequency domain in units of blocks having a preset size, obtain correlation information indicating a correlation between at least one block of the first frequency coefficient information and a first kernel, generate a weight corresponding to the first frequency coefficient information based on the correlation information, generate second frequency coefficient information by rearranging coefficients included in the first frequency coefficient information, wherein the one or more of the coefficients having a same frequency is arranged into a same group, and obtain quality information of the first image based on the weight and the second frequency coefficient information.

Abstract: An image processing apparatus, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to obtain similarity information indicating a similarity between each pixel of a plurality of pixels included in a first image and an adjacent pixel of the each pixel; generate a weight map including weight information corresponding to the each pixel, based on the similarity information; generate a spatially variant kernel including a plurality of kernels corresponding to the plurality of pixels, based on the weight map and a spatial kernel including weight information based on a location relationship between the each pixel and the adjacent pixel; and generate a second image by applying the spatially variant kernel to the first image.

Abstract: According to various embodiments of the disclosure, an audio processing apparatus includes at least one processor configured to execute one or more instructions to obtain a second audio signal down-mixed from at least one first audio signal, obtain information related to error removal for the at least one first audio signal, de-mix the at least one first audio signal from the down-mixed second audio signal, and reconstruct the at least one first audio signal by applying the information related to the error removal for the at least one first audio signal to the at least one first audio signal de-mixed from the second audio signal. The information related to the error removal having been generated using at least one of an original signal power of the at least one first audio signal or a second signal power of the at least one first audio signal after decoding.

Abstract: An audio processing apparatus may obtain second audio signals corresponding to channels included in a second channel group from first audio signals corresponding to channels included in a first channel group, downsample at least one third audio signal corresponding to at least one channel identified based on a correlation with the second channel group from among the channels included in the first channel group, by using an artificial intelligence (AI) model, and generate a bitstream including the second audio signals corresponding to the channels included in the second channel group and the downsampled at least one third audio signal. The first channel group includes a channel group of an original audio signal, and the second channel group is constructed by combining at least two channels from among the channels included in the first channel group.

Abstract: A method of controlling an element of a display device is provided. At least one processor of the display device may be configured to determine a first chromaticity value corresponding to a first grayscale value of the element of the display device, and determine a first luminance value corresponding to the first chromaticity value, based on the first chromaticity value and a target with respect to a relationship between chromaticity and luminance. In addition, the at least one processor of the display device may be configured to determine a second grayscale value corresponding to the first luminance value, determine a second chromaticity value corresponding to the second grayscale value, determine a second luminance value corresponding to the second chromaticity value, based on the second chromaticity value and the target, and determine chromaticity and luminance calibration coefficients, based on the second luminance value.

Abstract: The present disclosure relates to an artificial intelligence (AI) system utilizing a machine learning algorithm, including deep learning and the like, and application thereof. In particular, an electronic device of the present disclosure comprises: a memory including at least one command; and a processor connected to the memory so as to control the electronic device, wherein, by executing the at least one command, the processor acquires an image, acquires a noise correction map for correction of noise of the image on the basis of configuration information of a camera having captured the image or brightness information of the image, and eliminates the noise of the image through the noise correction map. In particular, at least a part of an image processing method may use an artificial intelligence model having been acquired through learning according to at least one of a machine learning algorithm, a neural network algorithm, and a deep learning algorithm.

Abstract: An image processing apparatus is disclosed. The present image processing apparatus comprises: a memory for storing a low dynamic range (LDR) image and a processor for adjusting the brightness of the LDR image by means of a pixel-specific brightness ratio identified using a first parameter, and acquiring a high dynamic range (HDR) image by adding or subtracting a pixel-specific correction value identified using a second parameter in the brightness-adjusted LDR image.

Abstract: A video encoding method and apparatus and a video decoding method and apparatus are provided. The video encoding method includes: prediction encoding in units of a coding unit as a data unit for encoding a picture, by using partitions determined based on a first partition mode and a partition level, so as to select a partition for outputting an encoding result from among the determined partitions; and encoding and outputting partition information representing a first partition mode and a partition level of the selected partition. The first partition mode represents a shape and directionality of a partition as a data unit for performing the prediction encoding on the coding unit, and the partition level represents a degree to which the coding unit is split into partitions for detailed motion prediction.

Abstract: An image processing apparatus applies an image to a first learning network model to optimize the edges of the image, applies the image to a second learning network model to optimize the texture of the image, and applies a first weight to the first image and a second weight to the second image based on information on the edge areas and the texture areas of the image to acquire an output image.

Abstract: An image processing apparatus applies an image to a first learning network model to optimize the edges of the image, applies the image to a second learning network model to optimize the texture of the image, and applies a first weight to the first image and a second weight to the second image based on information on the edge areas and the texture areas of the image to acquire an output image.

Abstract: An image processing apparatus includes a memory storing one or more instructions, and a processor executing the one or more instructions stored in the memory, wherein the processor uses one or more deep neural networks to generate first feature maps from a first image, generate second feature maps for each of subpixels based on the first feature maps and position information of each of the subpixels arranged in a display, and determine result values for each of the subpixels based on the second feature maps.

Abstract: An image processing apparatus is disclosed. The present image processing apparatus includes an input unit to which an image is input; and a processor which extracts visual characteristics by reducing an input image and obtains a high-definition image by reflecting extracted visual characteristics on the input image. The disclosure relates to an artificial intelligence (AI) system and application thereof that simulate functions such as cognition and decision-making of a human brain using a machine learning algorithm such as deep learning.