Samsung Electronics Co., Ltd.

Tammy Lee

Woo-jin Han

Jianle Chen

Il-koo Kim

Woo-Jin Han

Jung-hye Min

Min-su Cheon

Sun-il Lee

Hae-kyung Jung

Kyo-hyuk Lee

Vadim Seregin

Jung-Hye Min

Byeong-doo Choi

Iljun Ahn

Elena Alshina

Alexander Alshin

Yoon-mi Hong

Jaeyeon Park

Sun-Il Lee

Nikolay Shlyakhov

Min-Su Cheon

Yoon-Mi Hong

Yongsup Park

Chang-Hyun Lee

Dae-sung Cho

Byeong-Doo Choi

Hanul Shin

Kyo-Hyuk Lee

Minsu Cheon

Soomin Kang

Hae-Kyung Jung

Jaesung Lee

Sang-rae Lee

Woong-Il Choi

Sangchul Ko

Yong-sup Park

Sunghee Hwang

Kyungrae Kim

Woohyun Nam

Hyunkwon Chung

Il-jun Ahn

Jungkyu Kim

Yoonjae Son

Ki-heum Cho

Youngchan Song

Donghyun Kim

Seung-hoon Han

Youngsu Moon

Chang-han Kim

Cheon An Lee

Hyunseung Lee

Woo-hyun Nam

Taegyoung Ahn

Kyungmin Lim

Chan-Yul Kim

Hee-Seok Oh

Ii-koo Kim

Jae-yeon Park

Jae-Hyun Kim

Jeong-Hoon Park

Chan-yul Kim

Eun-Young Lee

Jae-Sung Lee

Seung-soo Jeong

Kwanghyun Won

Bae-Keun Lee

Sunmin Kim

Changhan Kim

Sun-Mi Park

Mathew Manu

Jaehee Kwak

Woong-II Choi

Kyung-Min Lim

Tejas Nair

Kwangdon Kim

Dubok Park

Duck-yeon Kim

Kiheum Cho

Heeseok Oh

IIjun Ahn

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.

Apparatus and method for processing multi-channel audio signal

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.

Image processing apparatus and operation method thereof

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.

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.

Method and device for encoding intra prediction mode for image prediction unit, and method and device for decoding intra prediction mode for image prediction unit

A method of matching a voice for each object included in a video, includes: separating a plurality of voices in a video; determining a dissimilarity between the plurality of voices; selecting a partial duration in an entire duration of the video as a matching duration, based on the dissimilarity between the plurality of voices; matching, within the matching duration, the plurality of voices with a plurality of objects in the video respectively, based on mouth movements of the plurality of objects; and matching the plurality of voices with the plurality of objects respectively in the entire duration of the video, based on results of the matching between the plurality of voices and the plurality of objects within the matching duration.

Method of matching sound source for each object included in video, and computing device for performing the same

An image processing apparatus, including a processor configured to execute instructions stored in a memory to: obtain characteristic information of a first image, divide the characteristic information into a plurality of groups, input each group into a respective layer of a plurality of layers included in a convolutional neural network and perform a convolution operation using one or more kernels to obtain a plurality of pieces of output information, generate an attention map including weight information corresponding to each pixel included in the first image, based on the plurality of pieces of output information, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on the attention map and a spatial kernel including weight information according to a position relationship between the each pixel and a neighboring pixel, and generate a second image by applying the spatially variant kernel to the first image.

Image processing apparatus and operating method thereof

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.

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.

Image processing apparatus and operating method of the same

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.

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.

Image processing device and operating method thereof

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.

Apparatus and method for processing audio

A method of performing, by an electronic device, a convolution operation at a certain layer in a neural network includes: obtaining N pieces of input channel data; performing a first convolution operation by applying a first input channel data group including K pieces of first input channel data from among the N pieces of input channel data to a first kernel filter group including K first kernel filters; performing a second convolution operation by applying a second input channel data group including K pieces of second input channel data from among the N pieces of input channel data to a second kernel filter group including K second kernel filters; and obtaining output channel data based on the first convolution operation and the second convolution operation, wherein K is a natural number that is less than N.

Convolution operations utilizing nonzero padding data copied from input channel data

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.

Display device and method for controlling same

Provided is an image processing apparatus including a memory storing one or more instructions, and a processor executing the one or more instructions stored in the memory, wherein the processor retargets a current frame image, based on a first scale factor for a previous frame image, a second scale factor for the current frame image, and a weight determined based on previous frame image data and current frame image data.

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.

Image processing device and method for operating same

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Seoul, South Korea

Tammy Lee