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, 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: A video decoding method includes obtaining an initial value of a context model for a syntax element, determining, based on the initial value, slope size information, slope sign information, offset size information, and offset sign information of the context model, determining an initial probability of the context model based on the slope size information, the slope sign information, the offset size information, the offset sign information, and a quantization parameter, obtaining at least one bin of a symbol indicating the syntax element by performing context adaptive binary arithmetic coding (CABAC) decoding based on the initial probability of the context model, and reconstructing a video based on the at least one bin.

Abstract: A method of reconstructing an optical flow by using artificial intelligence (AI), including obtaining, from a bitstream, feature data of a current residual optical flow for a current image; obtaining the current residual optical flow by applying the feature data of the current residual optical flow to a neural-network-based first decoder; obtaining a current predicted optical flow based on at least one of a previous optical flow, feature data of the previous optical flow, and feature data of a previous residual optical flow; and reconstructing a current optical flow based on the current residual optical flow and the current predicted optical flow.

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: A video decoding method includes determining whether an ultimate motion vector expression (UMVE) mode is allowed for an upper data unit including a current block, when the UMVE mode is allowed for the upper data unit, determining whether the UMVE mode is applied to the current block, when the UMVE mode is applied to the current block, determining a base motion vector of the current block, determining a correction distance and a correction direction for correction of the base motion vector, determining a motion vector of the current block by correcting the base motion vector according to the correction distance and the correction direction, and reconstructing the current block based on the motion vector of the current block.

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: 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: A video decoding method includes determining whether an ultimate motion vector expression (UMVE) mode is allowed for an upper data unit including a current block, when the UMVE mode is allowed for the upper data unit, determining whether the UMVE mode is applied to the current block, when the UMVE mode is applied to the current block, determining a base motion vector of the current block, determining a correction distance and a correction direction for correction of the base motion vector, determining a motion vector of the current block by correcting the base motion vector according to the correction distance and the correction direction, and reconstructing the current block based on the motion vector of the current block.

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: In various examples, techniques for multi-dimensional tracking of objects using two-dimensional (2D) sensor data are described. Systems and methods may use first image data to determine a first 2D detected location and a first three-dimensional (3D) detected location of an object. The systems and methods may then determine a 2D estimated location using the first 2D detected location and a 3D estimated location using the first 3D detected location. The systems and methods may use second image data to determine a second 2D detected location and a second 3D detected location of a detected object, and may then determine that the object corresponds to the detected object using the 2D estimated location, the 3D estimated location, the second 2D detected location, and the second 3D detected location. The systems and method then generate, modify, delete, or otherwise update an object track that includes 2D state information and 3D state information.

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: An example method for training AI models for in-loop filters includes generating a training dataset by passing a video through a codec pipeline, extracting one or more predefined block features from the training dataset, creating a plurality of clusters based on the extracted one or more predefined block features from the training dataset, dividing the plurality of clusters into a sub-plurality of clusters based on the extracted one or more predefined block features and an intra-cluster variation threshold, and supplying the sub-plurality of clusters separately into a plurality of AI models based on the extracted one or more predefined block features.

Abstract: An image decoding method including: obtaining, from a bitstream, information related to a triangle prediction mode for a current block; splitting the current block into two triangular partitions, according to the information related to a triangle prediction mode; generating a merge list for a triangle prediction mode, according to a merge list generation method in a regular merge mode; selecting a motion vector for the two triangular partitions according to information indicating the motion vector from among motion vectors included in the merge list; obtaining, from a reference image, prediction blocks corresponding to the two triangular partitions, based on the motion vector; and reconstructing the current block, based on a final prediction block.

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: An image decoding method including: obtaining, from a bitstream, information related to a triangle prediction mode for a current block; splitting the current block into two triangular partitions, according to the information related to a triangle prediction mode; generating a merge list for a triangle prediction mode, according to a merge list generation method in a regular merge mode; selecting a motion vector for the two triangular partitions according to information indicating the motion vector from among motion vectors included in the merge list; obtaining, from a reference image, prediction blocks corresponding to the two triangular partitions, based on the motion vector; and reconstructing the current block, based on a final prediction block.

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: In various examples, techniques for multi-dimensional tracking of objects using two-dimensional (2D) sensor data are described. Systems and methods may use first image data to determine a first 2D detected location and a first three-dimensional (3D) detected location of an object. The systems and methods may then determine a 2D estimated location using the first 2D detected location and a 3D estimated location using the first 3D detected location. The systems and methods may use second image data to determine a second 2D detected location and a second 3D detected location of a detected object, and may then determine that the object corresponds to the detected object using the 2D estimated location, the 3D estimated location, the second 2D detected location, and the second 3D detected location. The systems and method then generate, modify, delete, or otherwise update an object track that includes 2D state information and 3D state information.

Abstract: An image decoding method including: obtaining, from a bitstream, information related to a triangle prediction mode for a current block; splitting the current block into two triangular partitions, according to the information related to a triangle prediction mode; generating a merge list for a triangle prediction mode, according to a merge list generation method in a regular merge mode; selecting a motion vector for the two triangular partitions according to information indicating the motion vector from among motion vectors included in the merge list; obtaining, from a reference image, prediction blocks corresponding to the two triangular partitions, based on the motion vector; and reconstructing the current block, based on a final prediction block.

Abstract: An image decoding method including: obtaining, from a bitstream, information related to a triangle prediction mode for a current block; splitting the current block into two triangular partitions, according to the information related to a triangle prediction mode; generating a merge list for a triangle prediction mode, according to a merge list generation method in a regular merge mode; selecting a motion vector for the two triangular partitions according to information indicating the motion vector from among motion vectors included in the merge list; obtaining, from a reference image, prediction blocks corresponding to the two triangular partitions, based on the motion vector; and reconstructing the current block, based on a final prediction block.