Abstract: A device comprises one or more processors configured to: obtain a value for a first laser, the value for the first laser indicating a number of probes in an azimuth direction of the first laser; decode a syntax element for a second laser, wherein the syntax element for the second laser indicates a difference between the value for the first laser and a value for the second laser, the value for the second laser indicating a number of probes in the azimuth direction of the second laser; determine the value for the second laser indicating the number of probes in the azimuth direction of the second laser based on the first value and the indication of the difference between the value for the first laser and the value for the second laser; and decode a point based on the number of probes in the azimuth direction of the second laser.

Abstract: An example device for coding video data includes memory configured to store the video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine whether a sequence parameter set of the video data refers to a video parameter set. Based on the sequence parameter set not referring to the video parameter set, the one or more processors are configured to determine a value of a first syntax element to be indicative of a profile-tier-layer structure being signaled in the sequence parameter set and code the video data based on the profile-tier-layer structure.

Abstract: A video decoder may be configured to determine that a block of video data is encoded using an intra prediction process that utilizes multiple intra prediction predictors; determine a set of reference lines for the intra prediction process; determine a first set of intra prediction predictors based on the set of reference lines; determine a second set of intra prediction predictors based on the set of reference lines; generate a fusion of predictors from the first set of intra prediction predictors and the second set of intra prediction predictors; and decode the block of video data using the fusion of predictors.

Abstract: An example device for encoding high dynamic range (HDR) video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: calculate a histogram for an image of the video data, the image being expressed in a linear light format; encode values for the histogram of the image expressed in the linear light format; and encode the image. Data for the histogram may be expressed in an array of variables having a size of 210×18 bits. The device may encode codewords representing values for bins of the histogram, where the codewords may be selected from a set of codewords for a PQ10 format for HDR images. The bins of the histogram may represent non-equal width ranges.

Abstract: An example method of decoding a point cloud includes selecting, from a plurality of predefined prediction modes, a prediction mode for performing predictive geometry coding of a position of a current node of the point cloud, wherein the plurality of prediction modes includes at least: a zero prediction mode, and a delta prediction mode; responsive to selecting the zero prediction mode: determining a radius, an azimuth, and a laser index of a parent node of the current node; inferring an azimuth and a laser index of a predicted position of the current node as the azimuth and the laser index of the parent node; inferring a radius of the predicted position to be a minimum radius value, wherein the minimum radius value is different than the radius of the parent node; and determining, based on the predicted position of the current node, the position of the current node.

Abstract: A video decoder can be configured to determine that a current block in a current picture of the video data is coded in an affine prediction mode; determine one or more control-point motion vectors (CPMVs) for the current block; identify an initial prediction block for the current block in a reference picture using the one or more CPMVs; determine a current template for the current block in the current picture; and determine an initial reference template for the initial prediction block in the reference picture; and perform a motion vector refinement process to determine a modified prediction block based on a comparison of the current template to the initial reference template.

Abstract: An example device includes memory configured to store the video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine at least one of a temporal candidate or a history-based candidate and determine at least one non-adjacent candidate, wherein the at least one non-adjacent candidate is from a unit that is not adjacent to a current prediction unit (PU). The one or more processors are configured to determine an advanced motion vector predictor (AMVP) candidate list including the at least one of the temporal candidate or the history-based candidate and the at least one non-adjacent candidate. The at least one non-adjacent candidate is added to the AMVP candidate list after the temporal candidate or before the history-based candidate. The one or more processors are configured to code the current PU based on the AMVP candidate list.

Abstract: An example device for filtering decoded video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: decode a picture of video data; code a value for a syntax element representing a neural network model to be used to filter a portion of the decoded picture, the value representing an index into a set of pre-defined neural network models, the index corresponding to the neural network model in the set of pre-defined neural network models; and filter the portion of the decoded picture using the neural network model corresponding to the index.

Abstract: Certain aspects of the present disclosure provide techniques for global motion modeling. Embodiments include receiving a first image and a second image from a camera attached to a moving object. Embodiments include identifying a pixel in the first image. Embodiments include determining, based on one or more parameters associated with the camera, a vector representing a range of locations in which a real-world point corresponding to the pixel is likely to be found in the second image, wherein the parameters associated with the camera comprise: a first parameter related to a location of the camera relative to a ground surface; a second parameter related to motion of the moving object; and a third parameter related to an orientation of the camera relative to the ground surface. Embodiments include determining, using the vector, a location of the real-world point in the second image.

Abstract: A G-PCC encoder and G-PCC decoder may quantize and scale, respectively, a position of a child node. The G-PCC encoder may control the precision of the quantization and scaling using a quantization parameter (QP) value and a parameter value k, wherein the parameter value k specifies a number of QP points per doubling of a scaling step size to be used at the G-PCC decoder.

Abstract: An example device for decoding video data includes one or more processors implemented in circuitry and configured to: generate an inter-prediction block for a current block of video data; generate an intra-prediction block for the current block of video data; generate a final prediction block for the current block of video data from the inter-prediction block and the intra-prediction block, including performing each of combined inter/intra prediction (CIIP) mode, overlapped block motion compensation (OBMC), and luma mapping with chroma scaling (LMCS) while generating the final prediction block; and decode the current block of video data using the final prediction block. To generate the final prediction block, the processors may perform LMCS on a first inter-prediction sub-block, combine the LMCS-mapped first inter-prediction sub-block with the intra-prediction block using CIIP, and perform OBMC between the first CIIP prediction block and a second inter-prediction sub-block.

Abstract: A video decoder configured to set a context index variable to a first value, wherein the first value for the context index variable is associated with a first context; context decode a first bin for a syntax element indicating a transform using the first context; determine a new value for the context index variable based on a value of the first bin; context decode a second bin for the syntax element indicating the transform using a context associated with the new value; determine an inverse transform from a set of inverse transform candidates based on the first bin and the second bin; and apply the inverse transform to a set of coefficients to determine a block of residual data.

Abstract: Provided are systems and methods for processing 360-degree video data. In various implementations, a spherical representation of a 360-degree video frame can be segmented into a top region, a bottom region, and a middle region. The middle region can be mapped into one or more rectangular areas of an output video frame. The top region can be mapped into a first rectangular area of the output video frame using a mapping that converts a square to a circle, such that pixels in the circular top region are expanded to fill the first rectangular region. The bottom region can be mapped into a second rectangular area of the output video frame such that pixels in the circular bottom region are expanded to fill the second rectangular region.

Abstract: An example method includes decoding, via a first syntax level of a video bitstream, a first deblocking filter control syntax element with a value that specifies whether deblocking filter information is present in a second syntax level of the bitstream; decoding, via the first syntax level of the bitstream, a second deblocking filter control syntax element with a value that specifies whether deblocking override is enabled; responsive to the first deblocking filter control syntax element specifying that the deblocking filter information is present in the second syntax level of the bitstream and regardless of the value of the second deblocking filter control syntax element, decoding, via the second syntax level, one or more syntax elements that specify deblocking filter information; and applying, based on the deblocking filter information, a deblocking filter to a block of the video data.

Abstract: A method of encoding point cloud data includes signaling at least one of: a first syntax element indicating that planar mode is disabled for a current node when angular mode is enabled; or a second syntax element indicating that angular mode is enabled for the current node; bypassing signaling of values for a planar mode for the current node in a condition where the first syntax element indicates that the planar mode is disabled for the current node when the angular mode is enabled, or in a condition where the second syntax element indicates that the angular mode is enabled for the current node; and encoding the current node in a mode other than the planar mode.

Abstract: A video encoder and/or video decoder are configured to apply an adaptive loop filter to a reconstructed block of video data. The video encoder and/or video decoder may be configured to determine a value of a gradient for a sample in the reconstructed block of video data, including performing a gradient calculation for the sample using an available sample that corresponds to an unavailable sample that is outside of one of: a picture, a slice, a tile, or a tile group that includes the reconstructed block of samples, determine an adaptive loop filter for the reconstructed block of video data based at least in part on the determined value of the gradient for the sample, and apply the determined adaptive loop filter to the reconstructed block of video data to generate a filtered block of video data.

Abstract: A method of decoding video data includes determining an intra prediction mode from a plurality of intra prediction modes for a current block of the video data, determining a low frequency non-separable transform (LFNST) kernel from a plurality of LFNST kernels for the current block based on the determined intra prediction mode, wherein at least one LFNST kernel of the plurality of LFNST kernels is associated with at least two different intra prediction modes of the plurality of intra prediction modes, applying an inverse of the determined LFNST kernel to coefficient values generated from a transform unit (TU) of the current block to generate intermediate values, applying an inverse primary transform on the intermediate values to generate residual data, and reconstructing the current block based on the residual data.

Abstract: Systems and techniques are provided for processing video data. For example, the systems and techniques can include obtaining a current picture of video data and obtaining reference pictures for the current picture from the video data. A merge mode candidate can be determined for the current picture. First and second motion vectors can be identified for the merge mode candidate. A motion vector search strategy can be selected for the merge mode candidate from a plurality of motion vector search strategies. The selected motion vector search strategy can be associated with one or more constraints corresponding to at least one of the first motion vector or the second motion vector. The selected motion vector search strategy can be used to determine refined motion vectors based on the first motion vector, the second motion vector, and the reference pictures. The merge mode candidate can be processed using the refined motion vectors.

Abstract: An example device includes memory configured to store the video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine at least one of a temporal candidate or a history-based candidate and determine at least one non-adjacent candidate, wherein the at least one non-adjacent candidate is from a unit that is not adjacent to a current prediction unit (PU). The one or more processors are configured to determine an advanced motion vector predictor (AMVP) candidate list including the at least one of the temporal candidate or the history-based candidate and the at least one non-adjacent candidate. The at least one non-adjacent candidate is added to the AMVP candidate list after the temporal candidate or before the history-based candidate. The one or more processors are configured to code the current PU based on the AMVP candidate list.

Abstract: A point cloud encoder and decoder are configured to code point cloud data using predictive geometry coding and a vertical predictor. A vertical predictor is a previously coded point in the point cloud having a different laser ID compared to the currently coded point. The point cloud encoder and decoder may be configured to determine a pivot laser ID, determine a vertical predictor for a current point of the point cloud data, wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID, and code the current point using the vertical predictor and predictive geometry decoding.