Samsung Electronics Co., Ltd.

Yonsei University

Young-o Park

Kwang-pyo Choi

Il-koo Kim

Jeong-Hoon Park

Chan-yul Kim

Jae-hwan Kim

Jong-seok Lee

Sun-young Jeon

Jae-Hyun Kim

Byeong-doo Choi

Min-su Cheon

Sung-jin Kim

Young-hun Joo

Jae-Hwan Kim

Sang-ha Kim

Kwan-woong Song

Chan-Yul Kim

Jae-yun Jung

Hee-Chul Yang

Hee-chul Yang

Tae-kwon Chang

Sung-kweon Park

Jong-Seok Lee

Dosik Hwang

Jinseong Jang

Sung-jae Ko

Hyung-min Nam

Kihun Bang

Hanbyol Jang

Won-young Jang

Il-Koo Kim

Kwang-Pyo Choi

A method of determining a reference picture set (RPS), which is a set of reference pictures used in predictive decoding of a current picture that is to be decoded includes: obtaining a flag indicating whether the RPS is determined based on POC values of the current picture and a previous picture or whether the RPS is determined based on an index of a reference RPS, which is an identification value of the reference RPS that is one of pre-defined RPSs and is referred to in determining the RPS, and a delta RPS that is a difference value between a POC vale of a reference picture included in the reference RPS and a POC value of a reference picture included in the RPS; and determining the RPS according to a value of the flag.

Method and apparatus for determining reference picture set of image

Provided are an inter prediction method and a motion compensation method. The inter prediction method includes: performing inter prediction on a current image by using a long-term reference image stored in a decoded picture buffer; determining residual data and a motion vector of the current image generated via the inter prediction; and determining least significant bit (LSB) information as a long-term reference index indicating the long-term reference image by dividing picture order count (POC) information of the long-term reference image into most significant bit (MSB) information and the LSB information.

Method for inter prediction and device therefor, and method for motion compensation and device therefor

A method of determining a reference picture set (RPS), which is a set of reference pictures used in predictive decoding of a current picture that is to be decoded includes: obtaining a flag indicating whether the RPS is determined based on picture order count (POC) values of the current picture and a previous picture or whether the RPS is determined based on an index of a reference RPS, which is an identification value of the reference RPS that is one of pre-defined RPSs and is referred to in determining the RPS, and a delta RPS that is a difference value between a POC vale of a reference picture included in the reference RPS and a POC value of a reference picture included in the RPS; and determining the RPS according to a value of the flag.

Provided are a video encoding method and apparatus having temporal scalability, and a video decoding method and apparatus having temporal scalability. The video encoding method includes: splitting pictures included in a picture sequence into temporal sub-layers; classifying, as a first temporal layer access picture or a second temporal layer access picture, a temporal layer access picture based on whether a picture encoded after the temporal layer access picture is capable of referring to a picture encoded before the temporal layer access picture; and adding, to transmission unit data including the temporal layer access picture, type syntax information for identifying the first temporal layer access picture and the second temporal layer access picture, wherein the picture encoded after the temporal layer access picture belongs to a same temporal sub-layer as the temporal layer access picture or belongs to an upper temporal sub-layer to the temporal layer access picture.

Method and apparatus for coding video having temporal scalability, and method and apparatus for decoding video having temporal scalability

Provided is in-loop filtering technology using a trained deep neural network (DNN) filter model. An image decoding method according to an embodiment includes receiving a bitstream of an encoded image, generating reconstructed data by reconstructing the encoded image, obtaining information about a content type of the encoded image from the bitstream, determining a deep neural network (DNN) filter model trained to perform in-loop filtering by using at least one computer, based on the information about the content type, and performing the in-loop filtering by applying the reconstructed data to the determined DNN filter model.

Method and device for encoding or decoding image

Provided is an image encoding or decoding method and device capable of increasing image compression efficiency by determining a differential quantization parameter of a current block based on statistical information of original samples of the current block or samples reconstructed previously to the current block. The image decoding method includes receiving a bitstream, obtaining a predicted quantization parameter value and a quantized transformation coefficient of a current block from the bitstream, obtaining a differential quantization parameter of the current block based on statistical information of samples reconstructed previously to the current block, obtaining a quantization parameter of the current block based on the differential quantization parameter of the current block and the predicted quantization parameter value, and inversely quantizing the quantized transformation coefficient of the current block based on the quantization parameter of the current block.

Video encoding method and device and video decoding method and device

Provided is a prediction image generating technology using a deep neural network (DNN). Provided is an image decoding method including: receiving a bitstream of an encoded image; determining at least one block split from the encoded image; determining neighboring blocks for predicting a current block among the at least one block; generating prediction data of the current block by applying the neighboring blocks to a DNN learning model configured to predict a block of an image by using at least one computer; extracting residual data of the current block from the bitstream; and reconstructing the current block by using the prediction data and the residual data.

Provided are an image compressing method including determining a compressed image by performing downsampling using a deep neural network (DNN) on an image; determining a prediction signal by performing prediction based on the compressed image; determining a residual signal based on the compressed image and the prediction signal; and generating a bitstream comprising information about the residual signal, wherein the DNN has a network structure that is predetermined according to training of a downsampling process using information generated in an upsampling process, and an image compressing device for performing the image compressing method. Also, provided are an image reconstructing method of reconstructing a compressed image by using a DNN for upsampling, the compressed image having been compressed by the image compressing method, and an image reconstructing device for performing the image reconstructing method.

Method for encoding/decoding image and device therefor

Provided is a method of encoding an image, the method including: determining a subjective quality of the image when the image is compressed; determining at least one degree of compression that changes the subjective quality and is from among degrees of compression indicating how much the image is compressed; and encoding the image by compressing a residual signal of the image, based on compression information according to the determined degree of compression, wherein the subjective quality is determined for each frame by using a Deep Neural Network (DNN). Provided are an image decoding method and an image decoding apparatus for performing the image decoding method for decoding an image by using information encoded according to an image encoding method.

Method for encoding/decoding image, and device therefor

Provided is a method, performed by a device, of recognizing content, the method including selecting a template to be used for recognizing content displayed on the device when a type of a content service that provides the content to the device is determined; capturing a screen of the device when a control signal is received by the device; determining whether the captured screen corresponds to a gateway page; determining whether the captured screen corresponds to the selected template when the captured screen corresponds to the gateway page; and generating a template corresponding to the content service, based on the captured screen, when the captured screen does not correspond to the selected template.

Method and device for recognizing content

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Seoul, South Korea

Young-o Park