Abstract: A method of decoding point cloud data comprises obtaining a bitstream that is encoded to comply with one or more constraints and decoding the bitstream, wherein decoding the bitstream comprises: determining a residual value of a first component of an attribute of a point; generating a predicted value of a second component of the attribute of the point; and reconstructing the second component as a sum of the predicted value of the second component and a multiplication product of a scale factor for the second component and the residual value of the first component, wherein the constraints include a constraint that limits the first component of the attribute of the point, the residual value of the first component of the attribute of the point, the second component of the attribute, and the residual value of the second component of the attribute of the point to one or more predefined bitdepths.

Abstract: A video coder may code a sign prediction syntax element that indicates whether a sign prediction hypothesis is correct for a transform coefficient. The video coder may code the sign prediction syntax element using a context-based coding process. The video coder may determine a context for coding the sign prediction syntax element based on a position of the transform coefficient in the block of video data. The context may be further based on a coding mode used to code the block.

Abstract: Various embodiments may provide methods, systems, and devices for supporting application level discovery of Radio Access Network (RAN) statistics and/or events. Various embodiments may provide methods, systems, and devices for supporting application level signaling of Quality of Service (QoS) requirements.

Abstract: An example device for processing image data includes a memory configured to store an image; and one or more processors implemented in circuitry and configured to: process the image to identify a pilot signal in the image indicating a portion of the image, the pilot signal forming a boundary around the portion and having pixel values defined according to a mathematical relationship with pixel values within the portion such that the pilot signal is not perceptible to a human user and is detectable the device; determine the portion of the image using the pilot signal; and further process the portion to attempt to detect one or more contents of the portion without attempting to detect the one or more contents of the image in portions of the image outside the portion.

Abstract: A G-PCC coder is configured to receive the point cloud data, determine a final quantization parameter (QP) value for the point cloud data as a function of a node QP offset multiplied by a geometry QP multiplier, and code the point cloud data using the final QP value to create an coded point cloud.

Abstract: An example device for processing image data includes a memory configured to store an image; and one or more processors implemented in circuitry and configured to: process the image to identify a pilot signal in the image indicating a portion of the image, the pilot signal forming a boundary around the portion and having pixel values defined according to a mathematical relationship with pixel values within the portion such that the pilot signal is not perceptible to a human user and is detectable the device; determine the portion of the image using the pilot signal; and further process the portion to attempt to detect one or more contents of the portion without attempting to detect the one or more contents of the image in portions of the image outside the portion.

Abstract: An example method of encoding a point cloud includes determining one or more attribute parameters of an attribute of a point in the point cloud of a frame, wherein the one or more attribute parameters define how to determine or use a value of the attribute and are applicable to a plurality of points in the point cloud; and signaling, in a bitstream indicative of the point cloud, the one or more attribute parameters in a syntax structure that is specific to the frame.

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 method of encoding a point cloud includes determining that residual values for all components except one component of an attribute of a point in the point cloud are equal to zero; based on the determination that the residual values for all components except the one component of the attribute are equal to zero, determining a value for the one component that is equal to a magnitude of a residual value of the one component of the attribute minus an offset; encoding the value of the one component; and signaling the encoded value in a bitstream.

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: A method of decoding point cloud data comprises obtaining a bitstream that includes an arithmetically encoded syntax element indicating a vertical plane position of a planar mode of a node; and decoding the vertical plane position of the planar mode in the node, wherein decoding the vertical plane position of the planar mode comprises: determining a laser index of a laser candidate in a set of laser candidates, wherein the determined laser index indicates a laser beam that intersects the node; determining a context index based on whether the laser beam is above a first distance threshold, between the first distance threshold and a second distance threshold, between the second distance threshold and a third distance threshold, or below the third distance threshold; and arithmetically decoding the vertical plane position of the planar mode using a context indicated by the determined context index.

Abstract: In some examples, a method of decoding a point cloud includes decoding an initial QP value from an attribute parameter set. The method also includes determining a first QP value for a first component of an attribute of point cloud data from the initial QP value. The method further includes determining a QP offset value for a second component of the attribute of the point cloud data and determining a second QP value for the second component of the attribute from the first QP value and from the QP offset value. The method includes decoding the point cloud data based on the first QP value and further based on the second QP value.

Abstract: An example device for decoding point cloud data includes: a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: decode a frame of the point cloud data including a plurality of points, each of the points being associated with position values defining a respective position of the point; determine a global scaling factor for the frame; and scale the position values of each of the points by the global scaling factor. The scaling may be clipped to prevent the points exceeding the boundaries of a corresponding bounding box including respective points.

Abstract: In some examples, a method of decoding a point cloud includes determining a first slice QP value for a first component of an attribute in a slice of point cloud data. The method also includes decoding a first delta QP value for the first component of the attribute for a region in the slice and determining a first region QP value for the first component of the attribute in the region from the first slice QP value and from the first delta QP value. The method further includes decoding a second delta QP value for the second component of the attribute for the region and determining a second region QP value for the second component of the attribute in the region from the second delta QP value. The method includes decoding the point cloud data based on the first and second region QP values.

Abstract: A method comprises obtaining a first laser angle; obtaining a second laser angle; obtaining a laser angle difference for a third laser angle; determining a predicted value based on the first laser angle and the second laser angle; and determining the third laser angle based on the predicted value and the laser angle difference for the third laser angle.

Abstract: A method of decoding point cloud data comprises obtaining a bitstream that includes an arithmetically encoded syntax element indicating a vertical point position offset within a node of a tree that represents 3-dimensional positions of points in a point cloud represented by the point cloud data; and decoding the vertical point position offset, wherein decoding the vertical point position offset comprises: determining a laser index of a laser candidate in a set of laser candidates, wherein the determined laser index indicates a laser beam that intersects the node; determining a context index based on whether the laser beam is above a first distance threshold, between the first distance threshold and a second distance threshold, between the second distance threshold and a third distance threshold, or below the third distance threshold; and arithmetically decoding a bin of the vertical point position offset using a context indicated by the determined context index.

Abstract: An example device for decoding point cloud data includes memory configured to store the point cloud data and one or more processors implemented in circuitry and coupled to the memory. The one or more processors are configured to determine dimensions of a region box and determine dimensions of a slice bounding box. The one or more processors are also configured to decode a slice of the point cloud data associated with the slice bounding box. The dimensions of the region box are constrained to not exceed the dimensions of the slice bounding box.

Abstract: An example device for processing point cloud data includes a memory configured to store the point cloud data and one or more processors implemented in circuitry and coupled to the memory. The one or more processors are configured to count a number of edges of a cube of point cloud data comprising a vertex. The one or more processors are configured to set a variable based on a total of the counting. The one or more processors are also configured to process the point cloud data based on the variable.

Abstract: A method for decoding point cloud data comprises, based on a comparison of a maximum difference value and a threshold, applying an inverse function to a set of one or more jointly coded values to recover (i) residual values for attribute values of a current point of point cloud data and (ii) a predictor index that indicates a predictor in a predictor list, wherein predictors in the predictor list are based on attribute values of one or more neighbor points; determining predicted attribute values based on the predictor index; and reconstructing the attribute values of the current point based on the residual values and the predicted attribute values.

Abstract: An instantaneous decoder refresh, IDR, picture is encoded. The IDR picture occurs at a random access point in a bitstream of data, includes only intra-prediction slices, I-slices, and does not refer to any picture or pictures other than itself for prediction. When the IDR picture has a subsequent leading picture, a first Network Access Layer, NAL, unit type of the IDR picture is generated indicating that the IDR picture has a subsequent leading picture, and the first NAL unit type is encoded. When the IDR picture does not have a subsequent leading picture, a second Network Access Layer, NAL, unit type of the IDR picture is generated indicating that the IDR picture does not have a subsequent leading picture, the second NAL unit type being different from the first NAL unit type, and the second NAL unit type is encoded.