Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: A video decoding method for decoding an input bitstream in which each of pictures has been encoded with being split into a plurality of tiles, the method includes decoding partial decoding information included in the input bitstream and determining one or more target tiles to be decoded among the plurality of tiles according to the partial decoding information; and decoding video data corresponding to the one or more target tiles, wherein the partial decoding information includes at least one of first information indicating whether to perform partial decoding and second information indicating an area on which partial decoding is to be performed.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: A video decoding method for decoding an input bitstream in which each of pictures has been encoded with being split into a plurality of tiles, the method includes decoding partial decoding information included in the input bitstream and determining one or more target tiles to be decoded among the plurality of tiles according to the partial decoding information; and decoding video data corresponding to the one or more target tiles, wherein the partial decoding information includes at least one of first information indicating whether to perform partial decoding and second information indicating an area on which partial decoding is to be performed.

Abstract: A video decoding method for decoding an input bitstream in which each of pictures has been encoded with being split into a plurality of tiles, the method includes decoding partial decoding information included in the input bitstream and determining one or more target tiles to be decoded among the plurality of tiles according to the partial decoding information; and decoding video data corresponding to the one or more target tiles, wherein the partial decoding information includes at least one of first information indicating whether to perform partial decoding and second information indicating an area on which partial decoding is to be performed.

Abstract: A video decoding method for decoding an input bitstream in which each of pictures has been encoded with being split into a plurality of tiles, the method includes decoding partial decoding information included in the input bitstream and determining one or more target tiles to be decoded among the plurality of tiles according to the partial decoding information; and decoding video data corresponding to the one or more target tiles, wherein the partial decoding information includes at least one of first information indicating whether to perform partial decoding and second information indicating an area on which partial decoding is to be performed.

Abstract: Disclosed is a method and apparatus for encoding/decoding a video. According to an embodiment, provided is a method of setting a level for each of one or more regions, including decoding a definition syntax element related to level definition and a designation syntax element related to target designation from a bitstream; defining one or more levels based on the definition syntax element; and setting a target level designated by the designation syntax element among the defined levels for a target region designated by the designation syntax element.

Abstract: A video decoding apparatus decodes a sequence of one or more pictures in the unit of blocks which are split from each picture. The apparatus comprising a decoder configured to reconstruct, from a bitstream, a current motion vector difference of the current block among the blocks which belong to the sequence of one or more pictures, and an image decoder configured to predict and decode the current block using the motion vector of the current block. When the current motion vector difference of the current block is zero, the resolution of the current motion vector difference of the current block is set to the ¼ pixel precision without the information on the motion vector resolution extracted.

Abstract: A method for coding image information includes generating prediction information by predicting information on a current coding unit, and determining whether the information on the current coding unit is the same as the prediction information. When the information on the current coding unit is the same as the prediction information, a flag indicating that the information on the current coding unit is the same as the prediction information is coded and transmitted. When the information on the current coding unit is not the same as the prediction information, a flag indicating that the information on the current coding unit is not the same as the prediction information and the information on the current coding unit are coded and transmitted.

Abstract: The present invention relates to an image encoding and decoding technique, and more particularly, to an image encoder and decoder using unidirectional prediction. The image encoder includes a dividing unit to divide a macro block into a plurality of sub-blocks, a unidirectional application determining unit to determine whether an identical prediction mode is applied to each of the plurality of sub-blocks, and a prediction mode determining unit to determine a prediction mode with respect to each of the plurality of sub-blocks based on a determined result of the unidirectional application determining unit.

Abstract: Disclosed herein is a method for decoding a video including determining a coding unit to be decoded by block partitioning, decoding prediction syntaxes for the coding unit, the prediction syntaxes including a skip flag indicating whether the coding unit is encoded in a skip mode, after the decoding of the prediction syntaxes, decoding transform syntaxes including a transformation/quantization skip flag and a coding unit cbf, wherein the transformation/quantization skip flag indicates whether inverse transformation, inverse quantization, and at least part of in-loop filterings are skipped, and the coding unit cbf indicates whether all coefficients in a luma block and two chroma blocks constituting the coding unit are zero, and reconstructing the coding unit based on the prediction syntaxes and the transform syntaxes.

Abstract: The present invention relates to an apparatus and method for encoding and decoding an image by skip encoding. The image-encoding method by skip encoding, which performs intra-prediction, comprises: performing a filtering operation on the signal which is reconstructed prior to an encoding object signal in an encoding object image; using the filtered reconstructed signal to generate a prediction signal for the encoding object signal; setting the generated prediction signal as a reconstruction signal for the encoding object signal; and not encoding the residual signal which can be generated on the basis of the difference between the encoding object signal and the prediction signal, thereby performing skip encoding on the encoding object signal.

Abstract: Disclosed herein are a QTBT split structure allowing blocks of various shapes capable of more efficiently reflecting various local characteristics of video and a method of efficiently signaling the split structure.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: An eye tracking system for detecting position and movements of a user's eyes in a head-mounted display (HMD). The eye tracking system may include at least one near-IR (NIR) eye tracking camera positioned at each side of the user's face and pointed towards eye-facing surfaces of eyepieces of the HMD, an illumination source that emits NIR light towards the user's eyes, and hot mirrors located between the eye-facing surfaces of the eyepieces and the user's eyes. The hot mirrors reflect at least a portion of NIR light, while allowing visible light to pass. The cameras capture images of the user's eyes reflected by the hot mirrors. Positioning the hot mirrors at or near the eye-facing surfaces of the eyepieces allows the cameras to be placed at the sides of the user's face without having to image through the eyepieces.

Abstract: A wearable device includes a case, a bio-processor embedded in the case, and a plurality of electrodes connected to the bio-processor. The bio-processor is configured to selectively and respectively operate the electrodes as sensing electrodes and sourcing electrodes in response to a selection signal. The selected one/ones of the electrodes operated as sensing electrodes which pick up a biological signal from (e.g. biological activity or a biological condition of) the wearer. The selected one/ones of the electrodes operated as sourcing electrodes supply current to the wearer regulated to cause the desired biological signal to be picked up by the sensing electrode(s).