Abstract: A video decoding method and device for determining whether a prediction mode of a current block is an affine mode; splitting, when the prediction mode of the current block is the affine mode, a luma block of the current block into a plurality of sub luma blocks having a square shape based on a predefined sub block size; determining a mean luma motion vector for four neighboring sub luma blocks among the plurality of sub luma blocks, by using a motion vector of an upper-left sub luma block of the four sub luma blocks and a motion vector of a lower-right sub luma block of the four sub luma blocks; determining the mean luma motion vector to be a motion vector of a current sub chroma block corresponding to the four sub luma blocks; and performing prediction on the current sub chroma block by using the determined motion vector, in a video encoding and decoding process are suggested.

Abstract: A video decoding method includes determining, based on an area of a current block, whether a multi-prediction combination mode for predicting the current block by combining prediction results obtained according to a plurality of prediction modes is applied to the current block, when the multi-prediction combination mode is applied to the current block, determining the plurality of prediction modes to be applied to the current block, generating a plurality of prediction blocks of the current block, according to the plurality of prediction modes, and determining a combined prediction block of the current block, by combining the plurality of prediction blocks according to respective weights.

Abstract: Provided are a video decoding method and apparatus for, in a video encoding and decoding procedure, when a merge candidate list of a current block is configured, determining whether the number of merge candidates included in the merge candidate list is greater than 1 and is smaller than a predetermined maximum merge candidate number, when the number of the merge candidates included in the merge candidate list is greater than 1 and is smaller than the predetermined maximum merge candidate number, determining an additional merge candidate by using a first merge candidate and a second merge candidate of the merge candidate list of the current block, configuring the merge candidate list by adding the determined additional merge candidate to the merge candidate list, and performing prediction on the current block, based on the merge candidate list.

Abstract: An image encoding/decoding method and an apparatus thereof are provided. The decoding method includes: determining a coding unit by splitting an image; determining a transform unit in the coding unit; determining whether to decode residual data of the transform unit according to a transform skip mode; obtaining a significant subgroup flag about a subgroup in the transform unit; based on the significant subgroup flag indicating that the subgroup includes at least one non-zero significant coefficient and scanning information about coefficients of the subgroup, obtaining a first bin about a significant coefficient flag, a sign flag, a first flag, and a parity flag of a coefficient of a scan position; obtaining the residual data including a coefficient in the subgroup based on the base level and a remainder of an absolute value excluding the base level; and obtaining a reconstruction block of the coding unit, based on the residual data.

Abstract: An electronic device includes a display including a plurality of regions, a display driver integrated circuit configured to drive a first region of the display at a first driving frequency and drive a second region of the display at a second driving frequency according to a configuration of the display, and a processor configured to provide display data to the display driver integrated circuit. The processor identifies a driving frequency of a first screen to be output on the display in response to reception of a first input; and controls the display to output at least a portion of the first screen on a region, of the plurality of regions, driven at a driving frequency corresponding to the driving frequency of the first screen. The display optimizes driving frequency, thus reducing heating problems and resource waste in the electronic device.

Abstract: A case includes a base plate, a hole formed through the base plate, a pattern that is on one surface of the base plate and includes a plurality of crests and a plurality of troughs. The case further includes an inclined area that is between the hole and the pattern, and at least one step that is between the inclined area and the hole, in which a first angle that the inclined area forms with a central axis of the hole is greater than a second angle that is an angle of view through the hole.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.

Abstract: In various examples, surface profile estimation and bump detection may be performed based on a three-dimensional (3D) point cloud. The 3D point cloud may be filtered in view of a portion of an environment including drivable free-space, and within a threshold height to factor out other objects or obstacles other than a driving surface and protuberances thereon. The 3D point cloud may be analyzed—e.g., using a sliding window of bounding shapes along a longitudinal or other heading direction—to determine one-dimensional (1D) signal profiles corresponding to heights along the driving surface. The profile itself may be used by a vehicle—e.g., an autonomous or semi-autonomous vehicle—to help in navigating the environment, and/or the profile may be used to detect bumps, humps, and/or other protuberances along the driving surface, in addition to a location, orientation, and geometry thereof.

Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a parametric mathematical modeling. A variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface using varied parameters to simulate changes in road direction and lateral surface slope. In an example embodiment, a synthetic 3D road surface may be created by modeling a longitudinal 3D curve and expanding the longitudinal 3D curve to a 3D surface, and the resulting synthetic 3D surface may be sampled to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.

Abstract: In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

Abstract: Provided are an image decoding method and apparatus.

Abstract: Provided are video encoding/decoding methods and apparatuses for determining an intra prediction mode of a current block, based on a width and height of the current block. When the current block has a square shape in which the width and height are equal, the intra prediction mode of the current block is determined from among first intra prediction mode candidates including a plurality of predetermined intra prediction directions, and when the current block has a non-square shape in which the width and height are not equal, the intra prediction mode of the current block is determined from among second intra prediction mode candidates configured based on the non-square shape.

Abstract: An image encoding method based on artificial intelligence (AI) includes obtaining motion vector feature data of a current frame by inputting the current frame and a reference frame to a motion vector encoder and applying a plurality of resolution scales to the current frame and the reference frame via the motion vector encoder; obtaining a current motion vector by inputting the motion vector feature data to a motion vector decoder; obtaining a current predicted image based on the current motion vector and the reference frame; and generating a bitstream based on the motion vector feature data and the current predicted image.

Abstract: Provided is a video decoding method including: determining a prediction mode of a current block to be one of a skip mode and a merge mode; when a motion vector, which is determined from a merge candidate list of the current block, and a merge motion vector difference are to be used, obtaining merge candidate information indicating one candidate in the merge candidate list by performing entropy encoding on a bitstream by applying one piece of context information; determining a base motion vector from one candidate determined from the merge candidate list, based on the merge candidate information; and determining a motion vector of the current block by using a distance index of a merge motion vector difference of the current block and a direction index of the merge motion vector difference to use the base motion vector and the merge motion vector difference.

Abstract: Provided are a video decoding method and apparatus and a video encoding method and apparatus which determine whether to use a Most Probable Mode (MPM) list, based on encoding information of a current block, obtain, from a bitstream, intra prediction mode index information indicating one of candidate intra prediction modes included in the MPM list, and determine an intra prediction mode of the current block by using the obtained intra prediction mode index information.

Abstract: Provided are a video decoding method and apparatus for, in a video encoding and decoding procedure, when a merge candidate list of a current block is configured, determining whether the number of merge candidates included in the merge candidate list is greater than 1 and is smaller than a predetermined maximum merge candidate number, when the number of the merge candidates included in the merge candidate list is greater than 1 and is smaller than the predetermined maximum merge candidate number, determining an additional merge candidate by using a first merge candidate and a second merge candidate of the merge candidate list of the current block, configuring the merge candidate list by adding the determined additional merge candidate to the merge candidate list, and performing prediction on the current block, based on the merge candidate list.

Abstract: An electronic device includes: a housing including a plate and a back plate facing a second surface of the plate and including a planar area facing a first direction, a display module including a display disposed on a first surface of the plate, a battery and a circuit board disposed on the second surface of the plate, a first flexible substrate extending from the display module, and a second flexible substrate extending from the circuit board to the first flexible substrate across the battery. The first flexible substrate includes a first connector, and the second flexible substrate includes a second connector. The first flexible substrate and the second flexible substrate are configured such that the first connector and the second connector are coupled in a second direction different from the first direction.

Abstract: Provided is an image decoding method including: determining a first coding block and a second coding block corresponding to the first coding block; when a size of the first coding block is equal to or smaller than a preset size, obtaining first split shape mode information and second split shape mode information from a bitstream; determining a split mode of the first coding block, based on the first split shape mode information, and determining a split mode of the second coding block, based on the second split shape mode information; and decoding a coding block of a first color component which is determined based on the split mode of the first coding block and a coding block of a second color component which is determined based on the split mode of the second coding block.

Abstract: Provided are a video decoding method and apparatus and a video encoding method and apparatus which determine whether to use a Most Probable Mode (MPM) list, based on encoding information of a current block, obtain, from a bitstream, intra prediction mode index information indicating one of candidate intra prediction modes included in the MPM list, and determine an intra prediction mode of the current block by using the obtained intra prediction mode index information.

Abstract: Provided are a video decoding method and a video decoding device, which, during video encoding and decoding processes, obtain most probable mode (MPM) information indicating whether to use MPMs of a current block determined based on at least two of a prediction mode of a left neighboring block adjacent to a left side of the current block, a prediction mode of an upper neighboring block adjacent to an upper side of the current block, and a right neighboring block adjacent to a right side of the current block, obtain extended intra mode set information indicating whether to use an extended intra mode set configured based on the MPMs, and determine an intra prediction mode of the current block based on the MPM information and the extended intra mode set information.