A three-dimensional data encoding method includes: generating predicted position information using position information on three-dimensional points included in three-dimensional reference data associated with a time different from a time associated with current three-dimensional data; and encoding position information on three-dimensional points included in the current three-dimensional data, using the predicted position information.

Different implementations are described, particularly implementations for video encoding and decoding are presented including a method for deblocking an image. According to an implementation, in a method for deblocking an image, at least one boundary is determined between a first block of samples and a second block of samples: a boundary strength is determined according to at least one of a prediction mode of the first block and a prediction mode of the second block; and samples of the first and second blocks neighboring the at least one boundary are filtered according to the boundary strength. Advantageously, in case the prediction mode of the first block is a weighted prediction mode, the boundary strength further depends on the relative weight of samples used in predicting the first block of samples according to the weighted prediction mode of the first block and reciprocally for the second block.

A three-dimensional data encoding method includes: dividing three-dimensional points included in three-dimensional data into three-dimensional point sub-clouds including a first three-dimensional point sub-cloud and a second three-dimensional point sub-cloud; appending first information indicating a space of the first three-dimensional point sub-cloud to a header of the first three-dimensional point sub-cloud, and appending second information indicating a space of the second three-dimensional point sub-cloud to a header of the second three-dimensional point sub-cloud; and encoding the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud so that the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud are decodable independently of each other.

A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values are included in the compressed attribute information file. An order for the points is determined based on a space filling curve, wherein an encoder and a decoder determine a same order for the points based on the space filling curve. Levels of detail are determined by sampling the ordered points according to different sampling parameters, and attribute values are predicted for the points in the levels of detail using the determined order. The encoder determines attribute correction values based on a comparison of the predicted values to an original value prior to compression. The decoder corrects the predicted attribute values based on received attribute correction values.

A method for detecting a human skeleton is provided. The method includes: receiving a video frame, wherein the video frame comprises a human body; determining whether the video frame comprises prediction information; determining whether a first intra-coded macroblock (IMB) ratio of a target area comprising the human body in the video frame is greater than a first threshold when the video frame comprises the prediction information; and using a motion vector (MV) to estimate skeleton information of the human body when the first IMB ratio of the target area is not greater than the first threshold.

A point cloud data transmission device according to embodiments may comprise: an acquisition unit for acquiring point cloud data; an encoder for encoding the acquired point cloud data; and a transmitter for transmitting a bitstream including the encoded point cloud data. A point cloud data reception device according to embodiments may comprise: a receiver for receiving a bitstream including point cloud data; a decoder for decoding the point cloud data; and a renderer for rendering the point cloud data.

A point cloud data transmission method, according to embodiments, may comprise the steps of: encoding point cloud data; and transmitting the point cloud data. The present invention, according to embodiments, may comprise the steps of: receiving point cloud data; and decoding the point cloud data.

The present invention relates to a technique for encoding and decoding video data, and more particularly, to a method for performing inter-prediction in an effective manner. The present invention combines an inter-prediction method using an AMVP mode and an inter-prediction method using a merge mode so as to propose a method for using the same candidate. The method for encoding video data proposed by the present invention comprises the following steps: receiving mode information on an inter-prediction method of a current block; determining, on the basis of the received mode information, whether the interprediction method to be applied to the current block is an AMVP mode or a merge mode; and selecting a candidate to derive motion information of the current block, wherein the candidate is selected in a left region, top region and corner region of the current block and in the same position block as the current block, and the AMVP mode and the merge mode are applied on the basis of the selected candidate.

In one embodiment, a method includes determining characteristics of one or more areas in an image by analyzing pixels in the image, computing a sampling density for each of the one or more areas in the image based on the characteristics of the one or more areas, generating samples corresponding to the image by sampling pixels in each of the one or more areas according to the associated sampling density, and providing the samples to a machine-learning model as an input, where the machine-learning model is configured to reconstruct the image by processing the samples.

A device for decoding encoded point cloud data can be configured to: for a point of a point cloud, determine a first attribute value for a first color component based on a first predicted value and a first residual value; apply a scaling factor to the first residual value to determine a predicted second residual value, wherein the scaling factor has one or both of a non-integer value or an absolute value greater than one; for the point of the point cloud, receive a second residual value in the encoded point cloud data; determine a final second residual value based on the predicted second residual value and the received second residual value; and for the point of the point cloud, determine a second attribute value for a second color component based on a second predicted value and the final second residual value.