A point cloud data transmission method according to embodiments comprises the steps of: encoding point cloud data; and transmitting signaling data and the encoded point cloud data, wherein the step for encoding may comprise the steps of: dividing the point cloud data into a plurality of compression units; sorting, for each compression unit, the point cloud data in each compression unit; generating a prediction tree on the basis of the sorted point cloud data in the compression units; and compressing the point cloud data in the compression units by predicting on the basis of the prediction tree.

A three-dimensional data storage method includes: acquiring one or more units in which an encoded stream generated by encoding point cloud data is stored; and storing the one or more units into a file. The storing includes storing, in control information for the file, information indicating that data stored in the file is data generated by encoding the point cloud data.

An image decoding method includes: acquiring, from a bitstream, luma weight L0 flag information indicating whether there is an L0 prediction-related weight factor and luma weight L1 flag information indicating whether there is an L1 prediction-related weight factor; determining to apply decoder-side motion vector refinement (DMVR) to an L0 motion vector and L1 motion vector for a current block, when the luma weight L0 flag information and the luma weight L1 flag information are both zero; when it has been determined to apply DMVR, deriving a refined L0 motion vector and a refined L1 motion vector by applying the DMVR to the current block; deriving prediction samples for the current block on the basis of L0 prediction using the refined L0 motion vector and L1 prediction using the refined L1 motion vector; and generating reconstruction samples for the current block on the basis of the prediction samples.

A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.

A point cloud encoding method comprises: processing position information of a target point in a point cloud to obtain reconstruction information of the position information of the target point; obtaining an initial predicted value of attribute information of the target point according to the reconstruction information of the position information of the target point; filtering the initial predicted value of the attribute information of the target point using a Kalman filter algorithm to obtain a final predicted value of the attribute information of the target point; processing the attribute information of the target point in the point cloud to obtain a real value of the attribute information of the target point; obtaining a residual value of the attribute information of the target point according to the final predicted value and the real value of the attribute information of the target point; and encoding the residual value of the attribute information of the target point to obtain a bitstream.

In encoding an image, a transform skip flag that is flag information indicating, for each component, whether or not to skip transform processing of transforming a residual between an image and a predicted image of the image into coefficient data is generated, the transform skip flag generated is encoded, coded data of the transform skip flag is generated, and a bit stream including the generated coded data of the transform skip flag is generated. The present encoding/decoding can be applied to, for example, an image processing device, an image encoding device, an image decoding device, a transmission device, a reception device, a transmission-reception device, an information processing device, an imaging device, a reproduction device, a bit stream generation method, a coefficient data generation method, a quantization coefficient generation method, or the like.

In encoding an image, a transform skip flag that is flag information indicating, for each component, whether or not to skip transform processing of transforming a residual between an image and a predicted image of the image into coefficient data is generated, the transform skip flag generated is encoded, coded data of the transform skip flag is generated, and a bit stream including the generated coded data of the transform skip flag is generated. The present encoding/decoding can be applied to, for example, an image processing device, an image encoding device, an image decoding device, a transmission device, a reception device, a transmission-reception device, an information processing device, an imaging device, a reproduction device, a bit stream generation method, a coefficient data generation method, a quantization coefficient generation method, or the like.

A computer-implemented method comprises receiving a first compressed representation of a texture map in a first compression format, wherein the first compressed representation has been compressed using a first compressor, and receiving an array of compression parameters for a second compressor, the array of compression parameters including one or more respective compression parameters for each of a plurality of pixel regions of the texture map. The method further comprises decompressing the first compressed representation of the texture map to obtain the texture map, and compressing, using the second compressor, the texture map to a second compressed representation in a second compression format, comprising compressing each of said plurality of pixel regions of the texture map in accordance with the respective one or more compression parameters. The method further comprises storing the second compressed representation of the texture map to one or more memories accessible by a graphics processing unit, and selectively decompressing portions of the second compressed representation of the texture map using the graphical processing unit.

A computer-implemented method comprises receiving a first compressed representation of a texture map in a first compression format, wherein the first compressed representation has been compressed using a first compressor, and receiving an array of compression parameters for a second compressor, the array of compression parameters including one or more respective compression parameters for each of a plurality of pixel regions of the texture map. The method further comprises decompressing the first compressed representation of the texture map to obtain the texture map, and compressing, using the second compressor, the texture map to a second compressed representation in a second compression format, comprising compressing each of said plurality of pixel regions of the texture map in accordance with the respective one or more compression parameters. The method further comprises storing the second compressed representation of the texture map to one or more memories accessible by a graphics processing unit, and selectively decompressing portions of the second compressed representation of the texture map using the graphical processing unit.

An online system receives video data items from users and encodes the video data items using various codecs. To account for different computational resources used for encoding using different codecs, the online system ranks combinations of video data items by ratios of encoding video data items with different codecs to computational costs of encoding different video data items with different codecs. The benefit of encoding a video data item with a codec is based on a compression efficiency of the codec and a predicted aggregate amount of the video data item displayed to various users of the online system. Encoding video data items with codecs based on the determined ratios allows the online system to optimize a duration of video data having at least a threshold video quality to users.