Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

Abstract: An ionic wind generator and an electronic device having a heat dissipation function using the same are proposed. The ionic wind generator includes a power module; a first electrode configured to receive power from the power module by being connected to the power module to become an emitter electrode; and a second electrode spaced apart from the first electrode and grounded and at the same time connected to the power module to become a counter electrode. In addition, the first electrode is configured as a carbon brush including multiple carbon fibers. Accordingly, in the ionic wind generator, the emitter electrode is configured as the carbon brush having multiple carbon fibers.

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: Provided are a method and apparatus, which, during video encoding and decoding processes, obtain chroma intra prediction mode information about a current chroma block, when the chroma intra prediction mode information indicates a direct mode (DM), determine a luma block including a luma sample corresponding to a chroma sample at a lower-right location with respect to a center of the current chroma block, determine a chroma intra prediction mode of the current chroma block based on an intra prediction mode of the determined luma block, and perform intra prediction on the current chroma block, based on the determined chroma intra prediction mode.

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 artificial intelligence (AI)-based image decoding method and an apparatus performing the AI-based image decoding method are provided. According to the AI-based image decoding method, a transform block for a residual block of a current block is obtained from a bitstream, a transform kernel for the transform block is generated by applying, to a neural network, a prediction block for the current block, neighboring pixels of the current block, and coding context information, the residual block is obtained by applying the generated transform kernel to the transform block, and the current block is reconstructed by using the residual block and the prediction block.

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: 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.

Abstract: A video decoding method includes predicting a current block according to an intra prediction mode of the current block, determining whether to apply position dependent intra prediction filtering to the current block according to the intra prediction mode of the current block, when the position dependent intra prediction filtering is applied to the current block, determining at least one of an upper reference sample, a left reference sample, an upper weight, and a left weight for the position dependent intra prediction filtering of a current sample of the current block according to the intra prediction mode of the current block, and applying the position dependent intra prediction filtering to the current sample of the current block according to at least one of the upper reference sample, the left reference sample, the upper weight, and the left weight.

Abstract: A geometric approach may be used to detect objects on a road surface. A set of points within a region of interest between a first frame and a second frame are captured and tracked to determine a difference in location between the set of points in two frames. The first frame may be aligned with the second frame and the first pixel values of the first frame may be compared with the second pixel values of the second frame to generate a disparity image including third pixels. One or more subsets of the third pixels that have a value above a first threshold may be combined, and the third pixels may be scored and associated with disparity values for each pixel of the one or more subsets of the third pixels. A bounding shape may be generated based on the scoring.

Abstract: Provided are a method and apparatus, which, during video encoding and decoding processes, obtain chroma intra prediction mode information about a current chroma block, when the chroma intra prediction mode information indicates a direct mode (DM), determine a luma block including a luma sample corresponding to a chroma sample at a lower-right location with respect to a center of the current chroma block, determine a chroma intra prediction mode of the current chroma block based on an intra prediction mode of the determined luma block, and perform intra prediction on the current chroma block, based on the determined chroma intra prediction mode.

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 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: 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: 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: 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: 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 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 method of decoding an image according to an embodiment includes: when a size of a current block in the image is equal to or greater than a certain size, determining a candidate list including, as a candidate, a first reference block indicated by a temporal motion vector; when the first reference block is selected from among candidates included in the candidate list, determining motion vectors of sub-blocks in the current block by using motion vectors obtained from the first reference block; and reconstructing the current block based on sample values of a second reference block indicated by the motion vectors of the sub-blocks.

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.