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: An image processing method and an image processing apparatus are provided to obtain input data for deblocking filtering based on deblocking filtering target pixels of at least one line perpendicular to a boundary line of blocks and encoding information about the deblocking filtering target pixels of at least one line, obtain a feature map of the input data by inputting the input data to a first neural network, obtain a deblocking filter coefficient by inputting the feature map to a second neural network, obtain a deblocking filter compensation value by inputting the feature map to a third neural network, obtain a deblocking filter strength by inputting the input data to a fourth neural network, obtain deblocking filtered pixels by performing deblocking filtering on the deblocking filtering target pixels of the at least one line using the deblocking filter coefficient, the deblocking filter compensation value, and the deblocking filter strength.

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 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.

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: 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: Provided is a video decoding method including: generating a merge candidate list including neighboring blocks referred to predict a motion vector of a current block in a skip mode or a merge mode; when a merge motion vector difference is used according to merge difference mode information indicating whether the merge motion vector difference and a motion vector determined from the merge candidate list are used, determining a base motion vector from a candidate determined among the merge candidate list based on merge candidate information; determining the motion vector of the current block by using the base motion vector and a merge motion vector difference of the current block, the merge motion vector difference being determined by using a distance index and direction index of the merge motion vector difference of the current block; and reconstructing the current block by using the motion vector of the current block.

Abstract: Disclosed are a communication device for performing beamforming and an operating method thereof. The communication device includes: an antenna array configured to simultaneously form a first vertical beam and a second vertical beam for an arbitrary time; a transceiver configured to transmit and receive a signal using a double beam including the first vertical beam and the second vertical beam; and a processor configured to determine a first optimal vertical tilting angle of the first vertical beam based on Inter-Beam Interference (IBI) of the second vertical beam, and determine a second optimal vertical tilting angle of the second vertical beam based on the IBI of the first vertical beam.

Abstract: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

Abstract: Provided are a video decoding method and apparatus, in which, during video encoding and decoding processes: in which a first bin of a sub-block merge index indicating a candidate motion vector of a sub-block merge mode is obtained, the first bin being arithmetic-encoded using a context model, a second arithmetic-encoded in a bypass mode is obtained based on a first value obtained by arithmetic-decoding the first bin by using the context model, a second value is obtained by arithmetic-decoding the second bin in the bypass mode, and prediction on a current block is performed in the sub-block merge mode, based on the first value and the second value.

Abstract: In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.

Abstract: Provided is an image decoding method including determining a current chroma block having a rectangular shape corresponding to a current luma block included in one of a plurality of luma blocks, determining a piece of motion information for the current chroma block and a chroma block adjacent to the current chroma block by using motion information of the current chroma block and the adjacent chroma block, and performing inter prediction on the current chroma block and the adjacent chroma block by using the piece of motion information for the current chroma block and the adjacent chroma block to generate prediction blocks of the current chroma block and the adjacent chroma block.

Abstract: A video decoding method includes obtaining information indicating a first difference between a maximum size of a coding unit having a 1:4 ratio and a size of a largest coding unit, determining the maximum size of the coding unit of having the 1:4 ratio by using the size of the largest coding unit and the first difference, determining a minimum size of the coding unit having the 1:4 ratio based on a minimum size of a coding unit, determining whether a coding having the 1:4 ratio may be generated by splitting a first coding unit based on the maximum size and the minimum size of the coding unit having the 1:4 ratio, and determining a second coding unit including the coding unit having the 1:4 ratio, from the first coding unit, and decoding the second coding unit.

Abstract: In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.

Abstract: In various examples, lanes may be grouped and a sign may be assigned to a lane in a group, then propagated to another lane in the group to associate semantic meaning corresponding to the sign with the lanes. The sign may be assigned to the most similar lane as quantified by a matching score subject to the lane meeting any hard constraints. Propagation of an assignment of the sign to a different lane may be based on lane attributes and/or sign attributes. Lane attributes may be evaluated and assignments of signs may occur for a lane as a whole, and/or for particular segments of a lane (e.g., of multiple segments perceived by the system). A sign may be a compound sign that is identified as individual signs, which are associated with one another. Attributes of the compound sign may provide semantic meaning used to operate a machine.

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: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

Abstract: Provided is an image decoding method including determining a current chroma block having a rectangular shape corresponding to a current luma block included in one of a plurality of luma blocks, determining a piece of motion information for the current chroma block and a chroma block adjacent to the current chroma block by using motion information of the current chroma block and the adjacent chroma block, and performing inter prediction on the current chroma block and the adjacent chroma block by using the piece of motion information for the current chroma block and the adjacent chroma block to generate prediction blocks of the current chroma block and the adjacent chroma block.

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.