Abstract: Provided are a video decoding method and apparatus which, during video encoding and decoding processes, determine whether a current block is in contact with an upper boundary of a largest coding unit including the current block, when it is determined that the current block is in contact with the upper boundary of the largest coding unit, determine an upper reference line of the current block as one reference line, when it is determined that the current block is not in contact with the upper boundary of the largest coding unit, determine the upper reference line of the current block based on N reference lines, and use the determined upper reference line.

Abstract: Disclosed is an image decoding method according to an embodiment, the image decoding method including: obtaining a first reference block and a second reference block, for bi-directional prediction of a current block; obtaining, from a bitstream, weight information for combining the first reference block with the second reference block; performing entropy decoding on the weight information to obtain a weight index; combining the first reference block with the second reference block according to a candidate value indicated by the weight index among candidate values included in a weight candidate group; and reconstructing the current block based on a result of the combining, wherein a first binary value corresponding to the weight index is entropy-decoded based on a context model, and the remaining binary value corresponding to the weight index is entropy-decoded by a bypass method.

Abstract: Disclosed is an image decoding method according to an embodiment, the image decoding method including: obtaining a first reference block and a second reference block, for bi-directional prediction of a current block; obtaining, from a bitstream, weight information for combining the first reference block with the second reference block; performing entropy decoding on the weight information to obtain a weight index; combining the first reference block with the second reference block according to a candidate value indicated by the weight index among candidate values included in a weight candidate group; and reconstructing the current block based on a result of the combining, wherein a first binary value corresponding to the weight index is entropy-decoded based on a context model, and the remaining binary value corresponding to the weight index is entropy-decoded by a bypass method.

Abstract: Disclosed is an image decoding method according to an embodiment, the image decoding method including: obtaining a first reference block and a second reference block, for bi-directional prediction of a current block; obtaining, from a bitstream, weight information for combining the first reference block with the second reference block; performing entropy decoding on the weight information to obtain a weight index; combining the first reference block with the second reference block according to a candidate value indicated by the weight index among candidate values included in a weight candidate group; and reconstructing the current block based on a result of the combining, wherein a first binary value corresponding to the weight index is entropy-decoded based on a context model, and the remaining binary value corresponding to the weight index is entropy-decoded by a bypass method.

Abstract: In various examples, systems and methods are disclosed that preserve rich, detail-centric information from a real-world image by augmenting the real-world image with simulated objects to train a machine learning model to detect objects in an input image. The machine learning model may be trained, in deployment, to detect objects and determine bounding shapes to encapsulate detected objects. The machine learning model may further be trained to determine the type of road object encountered, calculate hazard ratings, and calculate confidence percentages. In deployment, detection of a road object, determination of a corresponding bounding shape, identification of road object type, and/or calculation of a hazard rating by the machine learning model may be used as an aid for determining next steps regarding the surrounding environment—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.

Abstract: Disclosed is an image decoding method according to an embodiment, the image decoding method including: obtaining a first reference block and a second reference block, for bi-directional prediction of a current block; obtaining, from a bitstream, weight information for combining the first reference block with the second reference block; performing entropy decoding on the weight information to obtain a weight index; combining the first reference block with the second reference block according to a candidate value indicated by the weight index among candidate values included in a weight candidate group; and reconstructing the current block based on a result of the combining, wherein a first binary value corresponding to the weight index is entropy-decoded based on a context model, and the remaining binary value corresponding to the weight index is entropy-decoded by a bypass method.

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: 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, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN.

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: 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: Provided is a video decoding method including: obtaining, from a bitstream, intra prediction mode information indicating an intra prediction mode of a current block; determining an interpolation filter set to be used in prediction of the current block, based on at least one of a size of the current block and the intra prediction mode indicated by the intra prediction mode information; determining a reference location to which a current sample of the current block refers according to the intra prediction mode; determining, in the interpolation filter set, an interpolation filter that corresponds to the reference location; determining a prediction value of the current sample, according to reference samples of the current block and the interpolation filter; and reconstructing the current block, based on the prediction value of the current sample.

Abstract: Provided are a video decoding method and apparatus including: in a video encoding and decoding process, determining parity information of a current block based on a width and a height of the current block; determining a lookup table of the current block from among a plurality of predefined lookup tables based on the parity information; determining a dequantization scale value of the current block based on the lookup table of the current block; and performing dequantization on the current block by using the dequantization scale value.

Abstract: Provided is a video decoding method including: generating coding units by splitting at least one of a height and a width of a largest coding unit having a first size; based on whether a height or a width of a non-square first coding unit including an outer boundary of an image, among the coding units, is greater than a maximum transform size, determining whether it is allowed to generate two second coding units by splitting at least one of the height and the width of the first coding unit; and decoding the second coding units generated from the first coding unit.

Abstract: In various examples, perception-based parking assistance systems and methods for an ego-machine are presented. Example embodiments may determine a location of a real-world parking strip relative to an ego-machine and an associated parking rule for the parking strip. A virtual parking strip and one or more virtual parking signs may be generated based at least in part on one or more detected features in an environment of the ego-machine and a tracked motion of the ego machine, and the virtual parking strip may be used to track parking strip locations and associated parking rules. The virtual parking strips and associated rules may be relied upon by an ego-machine to determine parking locations and/or to navigate into a suitable parking spot.

Abstract: Provided is a video decoding method including: obtaining, from a bitstream, intra prediction mode information indicating an intra prediction mode of a current block; determining an interpolation filter set to be used in prediction of the current block, based on at least one of a size of the current block and the intra prediction mode indicated by the intra prediction mode information; determining a reference location to which a current sample of the current block refers according to the intra prediction mode; determining, in the interpolation filter set, an interpolation filter that corresponds to the reference location; determining a prediction value of the current sample, according to reference samples of the current block and the interpolation filter; and reconstructing the current block, based on the prediction value of the current sample.

Abstract: An entropy decoding method may include obtaining, from a bitstream, information about a slice type, based on an occurrence probability of a symbol, performing arithmetic decoding on a current symbol corresponding to a syntax element, when the information about the slice type indicates an I slice, determining a first scaling factor for updating the occurrence probability of the symbol by using a first function, wherein a value of the first function is determined based on a first threshold value, when the information about the slice type indicates a B or P slice, determining the first scaling factor by using a second function, wherein a value of the second function is determined based on a second threshold value, and by using the first scaling factor, updating the occurrence probability of the symbol.

Abstract: Provided is a video decoding method including: obtaining, from a bitstream, intra prediction mode information indicating an intra prediction mode of a current block; determining an interpolation filter set to be used in prediction of the current block, based on at least one of a size of the current block and the intra prediction mode indicated by the intra prediction mode information; determining a reference location to which a current sample of the current block refers according to the intra prediction mode; determining, in the interpolation filter set, an interpolation filter that corresponds to the reference location; determining a prediction value of the current sample, according to reference samples of the current block and the interpolation filter; and reconstructing the current block, based on the prediction value of the current sample.

Abstract: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.

Abstract: Provided are a video decoding method and apparatus including: in a video encoding and decoding process, determining parity information of a current block based on a width and a height of the current block; determining a lookup table of the current block from among a plurality of predefined lookup tables based on the parity information; determining a dequantization scale value of the current block based on the lookup table of the current block; and performing dequantization on the current block by using the dequantization scale value.