Samsung Electronics Co., Ltd.

Kwangpyo Choi

Youngo Park

Quockhanh Dinh

Jaehwan Kim

Jongseok Lee

Minseok Choi

Minwoo Park

Sunyoung Jeon

Yumi Sohn

Minsoo Park

Myungjin Eom

Yinji Piao

Kiho Choi

Woongil Choi

Sungchan Kim

Kyungah Kim

Yongsup Park

Chaeeun Lee

Sangwook Baek

Anish Tamse

Seungsoo Jeong

Narae Choi

Gahyun Ryu

Jaeyeon Park

Pilkyu Park

Heechul Yang

Nishant Sharma

Minsu Cheon

Raj Narayana Gadde

Sri Nitchith Akula

Amith Dsouza

Anubhav Singh

Ramkumaar Kk

Kyonghwan Jin

Chirag Mahesh Kumar Pujara

Akhil Konda

Dongkyu Kim

Sangwon Lee

Jungmin Lee

Iljun Ahn

Ilkoo Kim

Tejas Nair

Kiljong Kim

Sangmi Lee

Junghye Min

Gihyeon Bae

Taekeun Kang

Provided are a video decoding method and apparatus for obtaining, from a bitstream, information about a first motion vector of a current block, determining, based on the information about the first motion vector, the first motion vector, determining a candidate list including a plurality of candidate prediction motion vectors for determining a second motion vector of the current block, determining, based on a distance between each of the plurality of candidate prediction motion vectors and the first motion vector, one of the plurality of candidate prediction motion vectors as the second motion vector, and determining a motion vector of the current block by using the first motion vector and the second motion vector.

Method and apparatus for video decoding and video encoding using a plurality of candidate prediction motion vectors

An image decoding method and apparatus obtain intra prediction feature data of a current block from a bitstream, determine an intra flow indicating a reference pixel of a current pixel in the current block, by applying the intra prediction feature data, neighboring pixels of the current block, and coding context information of the current block to a neural network, obtain a predicted pixel of the current pixel based on the intra flow of the current block, and reconstruct the current block based on the predicted pixel.

Image decoding apparatus and method and image encoding apparatus and method

Provided are an image decoding method and apparatus. The image decoding method includes identifying at least two neighboring sub-blocks that have a same motion vector and are adjacent to each other from among a plurality of neighboring sub-blocks adjacent to a current block, determining a representative motion vector and representative location information corresponding to the at least two neighboring sub-blocks, determining a parameter of a model for determining a motion vector based on the representative motion vector and the representative location information, determining a motion vector of a current sub-block included in the current block, based on location information of the current sub-block and the parameter, and predicting the current block based on the motion vector of the current sub-block wherein the representative motion vector is determined based on the same motion vector, and the representative location information indicates coordinates that are determined based on the at least two neighboring sub-blocks.

Image decoding method and apparatus, and image encoding method and apparatus

A method of decoding an image, including obtaining a motion vector of a current block; obtaining a preliminary prediction block based on a reference block indicated by the motion vector in a reference image; obtaining a final prediction block for the current block by applying, to a neural network, at least one of a picture order count (POC) map including a POC difference between the reference image and a current image including the current block, the preliminary prediction block, and a quantization error map; and reconstructing the current block based on the final prediction block and a residual block obtained from a bitstream, wherein sample values of the quantization error map are calculated based on a quantization parameter for the reference block.

Method and apparatus for image decoding and image encoding using AI prediction block

A method of decoding an image based on cross-channel prediction using artificial intelligence (AI) includes obtaining cross-channel prediction information by applying feature data for cross-channel prediction to a neural-network-based cross-channel decoder, obtaining a predicted image of a chroma image by performing cross-channel prediction based on a reconstructed luma image and the cross-channel prediction information, obtaining a residual image of the chroma image by applying feature data of the chroma image to a neural-network-based chroma residual decoder, and reconstructing the chroma image based on the predicted image and the residual image.

Artificial intelligence-based image encoding apparatus and method and decoding apparatus and method

An image decoding method using artificial intelligence (AI), including obtaining, from a bitstream, a current optical flow and correction data which are generated based on a current predicted image and a current image; obtaining the current predicted image based on a previous reconstructed image and the current optical flow; obtaining feature data of the current predicted image by applying the current predicted image to a neural network-based predicted image encoder; and obtaining a current reconstructed image corresponding to the current image by applying the correction data and the feature data of the current predicted image to a neural network-based image decoder.

Image encoding apparatus and image decoding apparatus both using artificial intelligence, and image encoding method and image decoding method performed by the image encoding apparatus and the image decoding apparatus

Provided is an electronic device configured to participate in video conference by using artificial intelligence (AI), the electronic device including a display and a processor configured to execute one or more instructions. The processor configured to obtain, from a server, image data generated by first encoding first image related to another electronic device participating in video conference, and AI data related to AI downscaling from original image to first image, obtain second image corresponding to first image by performing first decoding on image data, determine whether to perform AI upscaling on second image, based on importance of the other electronic device, based on determining to perform AI upscaling, obtain third image by performing AI upscaling on second image through an upscaling deep neural network and provide third image to display, and based on determining not to perform AI upscaling, provide second image to display.

Method and apparatus for artificial intelligence downscaling and upscaling during video conference

Provided is a video decoding method including obtaining identification information of a first tile and identification information of a last tile from a bitstream, wherein the first and last tiles are included in a first slice, determining an index difference between the first tile and the last tile, based on a result of comparing the identification information of the first tile with the identification information of the last tile, determining the number of tiles included in the first slice, by using the index difference between the first tile and the last tile, and decoding a plurality of tiles included in the first slice according to an encoding order by using the number of tiles included in the first slice, the identification information of the first tile, and the identification information of the last tile.

Video encoding method and decoding method using tiles and slices, and video encoding device and decoding device using tiles and slices

An entropy decoding method may include obtaining, from a bitstream, information about a slice type, based on an occurrence probability of a symbol, performing arithmetic decoding on a current symbol corresponding to a syntax element, when the information about the slice type indicates an I slice, determining a first scaling factor for updating the occurrence probability of the symbol by using a first function, wherein a value of the first function is determined based on a first threshold value, when the information about the slice type indicates a B or P slice, determining the first scaling factor by using a second function, wherein a value of the second function is determined based on a second threshold value, and by using the first scaling factor, updating the occurrence probability of the symbol.

Entropy encoding and decoding apparatus and method for using the same

A method, performed by an image decoding apparatus, of decoding a motion vector, including obtaining information indicating a motion vector resolution of a current block from a bitstream; selecting a first neighboring block from among neighboring blocks adjacent to the current block, by using the obtained information indicating the motion vector resolution of the current block; based on the current block referring to a reference picture in a list 0, and the first neighboring block referring to the reference picture in the list 0, determining a prediction motion vector of the current block using a motion vector of the first neighboring block; based on the current block referring to the reference picture in the list 0 and the first neighboring block referring to a reference picture in a list 1, selecting a motion vector of a second neighboring block among the neighboring blocks as a basic motion vector, and determining the prediction motion vector of the current block using the determined basic motion vector; and determining a motion vector of the current block using the prediction motion vector of the current block.

Device and method for encoding motion vector, and device and method for decoding motion vector

An image processing method and an image processing apparatus are provided to obtain input data for deblocking filtering based on deblocking filtering target pixels of at least one line perpendicular to a boundary line of blocks and encoding information about the deblocking filtering target pixels of at least one line, obtain a feature map of the input data by inputting the input data to a first neural network, obtain a deblocking filter coefficient by inputting the feature map to a second neural network, obtain a deblocking filter compensation value by inputting the feature map to a third neural network, obtain a deblocking filter strength by inputting the input data to a fourth neural network, obtain deblocking filtered pixels by performing deblocking filtering on the deblocking filtering target pixels of the at least one line using the deblocking filter coefficient, the deblocking filter compensation value, and the deblocking filter strength.

Image processing method and apparatus using neural network based deblocking filtering

An image decoding method may include: receiving a bitstream generated as a result of encoding an image sequence; obtaining, from a sequence parameter set of the bitstream, a first tool set index indicating a tool allowed to decode the bitstream among a plurality of tools; obtaining, from the sequence parameter set, tool flags based on the tool flags, identifying a tool that has been used to encode the image sequence among the plurality of tools; and reconstructing the image sequence based on the identified tool, wherein values of the tool flags are set according to a value of the first tool set index.

Image decoding device using tool set and image decoding method thereby, and image encoding device and image encoding method thereby

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 cause the image processing apparatus to: identify, in a previous frame, a prediction sample corresponding to a current sample of a current frame, generate a prediction frame for the current frame by changing a sample value of a collocated sample of the previous frame according to a sample value of the prediction sample, derive a weight by comparing a sample value of the current sample with the sample value of the prediction sample, apply the weight to a collocated sample of the prediction frame to obtain a weighted prediction frame, and obtain a current output frame by processing the current frame and the weighted prediction frame through a neural network comprising a convolution layer.

Image processing apparatus and method of processing multi-frames using the same

Provided is an electronic apparatus configured to provide an image based on artificial intelligence (AI), the electronic apparatus including a processor configured to execute one or more instructions stored in the electronic apparatus to obtain a first image by AI-downscaling an original image by a downscaling neural network, obtain first image data by encoding the first image, based on a display apparatus not supporting an AI upscaling function, obtain a second image by decoding the first image data, obtain a third image by AI-upscaling the second image by an upscaling neural network, and provide, to the display apparatus, second image data obtained by encoding the third image.

Image providing method and apparatus using artificial intelligence, and display method and apparatus using artificial intelligence

An electronic apparatus includes: a display; and a processor configured to: obtain a first image, identify an application used to obtain the first image, select neural network setting information corresponding to the identified application from among a plurality of pieces of neural network setting information, obtain a second image by artificial intelligence (AI)-upscaling the first image by using an upscaling neural network to which the selected neural network setting information is applied, and output the obtained second image via the display.

Electronic apparatus and method for reproducing image based on artificial intelligence according to application

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Suwon-si, South Korea

Kwangpyo Choi