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.

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 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: 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: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

Abstract: In various examples, systems and methods are disclosed that detect hazards on a roadway by identifying discontinuities between pixels on a depth map. For example, two synchronized stereo cameras mounted on an ego-machine may generate images that may be used extract depth or disparity information. Because a hazard's height may cause an occlusion of the driving surface behind the hazard from a perspective of a camera(s), a discontinuity in disparity values may indicate the presence of a hazard. For example, the system may analyze pairs of pixels on the depth map and, when the system determines that a disparity between a pair of pixels satisfies a disparity threshold, the system may identify the pixel nearest the ego-machine as a hazard pixel.

Abstract: 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

Abstract: 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.

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: 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: 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, live perception from sensors of a vehicle may be leveraged to detect and classify intersections 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 various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

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: An artificial intelligence (AI)-based video decoding method comprises obtaining, from a bitstream, a joint chroma residual sample of a current block, Cb component prediction information of the current block, and Cr component prediction information of the current block; determining a prediction sample of the Cb component of the current block based on at least the Cb component prediction information; determining a prediction sample of the Cr component of the current block based on at least the Cr component prediction information; and reconstructing the current block by obtaining a reconstructed sample of the Cb component of the current block and a reconstructed sample of the Cr component of the current block from an output of a neural network by inputting the joint chroma residual sample, the prediction sample of the Cb component, and the prediction sample of the Cr component to the neural network.

Abstract: In various examples, lane biasing for navigating around objects in autonomous systems and applications is described. Systems and methods are disclosed that generate lane (or other demarcated regions of an environment) geometries in environments—such as environments without clear lane or boundary demarcations—by using locations and poses of static objects and/or outputs of a drivable free-space analysis. The systems and methods then use the lane geometries, the locations of the static objects, and current paths (e.g., centerlines of the current paths) along the lanes to determine new paths for navigating around the static objects. For instance, the new paths may be determined by shifting the centerlines of the current paths in directions away from the static objects by some distance or safety margin. This way, the vehicles may navigate around the static objects with greater distances between the vehicles and the static objects for increased safety.

Abstract: In a video encoding and decoding process, a video decoding method and a video decoding apparatus are provided to for determining whether a motion vector of an adjacent block at a location corresponding to one motion vector resolution among a plurality of motion vector resolutions is available, when the motion vector of the adjacent block is available, obtaining the motion vector of the adjacent block as a motion vector predictor of a current block, when the motion vector of the adjacent block is unavailable, obtaining a default motion vector by using a motion vector of one of two other adjacent blocks of the current block as a motion vector predictor of the current block, and performing prediction on the current block based on the motion vector predictor of the current block.

Abstract: An image decoding method includes reconstructing a current image by performing deblocking filtering on a boundary of at least one reconstruction block from among reconstruction blocks, wherein the reconstructing of the current image by performing the deblocking filtering on the boundary of the at least one reconstruction block from among the reconstruction blocks includes, when a prediction mode of at least one reconstruction mode from among blocks located on both sides of the boundary of the at least one reconstruction block is a combined inter-intra prediction mode, determining that a value of a boundary filtering strength applied to the boundary of the at least one reconstruction block is a predetermined value and performing deblocking filtering on the boundary of the at least one reconstruction block based on the determined value of the boundary filtering strength.

Abstract: In various examples, feature values corresponding to a plurality of views are transformed into feature values of a shared orientation or perspective to generate a feature map—such as a Bird's-Eye-View (BEV), top-down, orthogonally projected, and/or other shared perspective feature map type. Feature values corresponding to a region of a view may be transformed into feature values using a neural network. The feature values may be assigned to bins of a grid and values assigned to at least one same bin may be combined to generate one or more feature values for the feature map. To assign the transformed features to the bins, one or more portions of a view may be projected into one or more bins using polynomial curves. Radial and/or angular bins may be used to represent the environment for the feature map.

Abstract: Provided is a video decoding method including: obtaining, from a sequence parameter set, sequence merge mode with motion vector difference (sequence MMVD) information indicating whether an MMVD mode is applicable in a current sequence; when the MMVD mode is applicable according to the sequence MMVD information, obtaining, from a bitstream, first MMVD information indicating whether the MMVD mode is applied in a first inter prediction mode for a current block included in the current sequence; when the MMVD mode is applicable in the first inter prediction mode according to the first MMVD information, reconstructing a motion vector of the current block which is to be used in the first inter prediction mode, by using a distance of a motion vector difference and a direction of a motion vector difference obtained from the bitstream; and reconstructing the current block by using the motion vector of the current block.

Abstract: A pixel of a display apparatus includes a light emitting device and a pixel circuit connected to first to third gate control lines and the light emitting device, the pixel circuit including first to fourth nodes. The pixel circuit includes a driving transistor connected to the first to third nodes, a first transistor connected to the first gate control line and the first and second nodes, a second transistor connected to the second gate control line, the second node, and a first driving voltage line, a third transistor connected to the first gate control line, the third node, and the fourth node, a fourth transistor connected to the first gate control line, the fourth node, and an initialization voltage line, a fifth transistor connected to the third gate control line, the third node, and a data line, and a storage capacitor between the first node and the fourth node.