A compression device for compressing image data generated by a computed tomography (CT) imaging system is described herein. The compression device is configured to compress the image data by implementing a method including receiving image data from the CT imaging system and requantizing the image data in a square root domain. The method further includes identifying a group of projections (GOP) in the image data, including a first projection and a plurality of subsequent projections, and performing spatial-delta encoding on the first projection and temporal-delta encoding on each of the plurality of subsequent projections. The method also includes identifying a signed value in the GOP, and converting the signed value to an unsigned value. The method further includes entropy coding the image data in the GOP, and packetizing the GOP for transmission or storage.

The present invention relates to a method and device for sharing a candidate list. A method of generating a merging candidate list for a predictive block may include: producing, on the basis of a coding block including a predictive block on which a parallel merging process is performed, at least one of a spatial merging candidate and a temporal merging candidate of the predictive block; and generating a single merging candidate list for the coding block on the basis of the produced merging candidate. Thus, it is possible to increase processing speeds for coding and decoding by performing inter-picture prediction in parallel on a plurality of predictive blocks.

The present invention relates to a method and device for sharing a candidate list. A method of generating a merging candidate list for a predictive block may include: producing, on the basis of a coding block including a predictive block on which a parallel merging process is performed, at least one of a spatial merging candidate and a temporal merging candidate of the predictive block; and generating a single merging candidate list for the coding block on the basis of the produced merging candidate. Thus, it is possible to increase processing speeds for coding and decoding by performing inter-picture prediction in parallel on a plurality of predictive blocks.

A moving picture coding apparatus includes an intra-inter prediction unit which calculates a second motion vector by performing a scaling process on a first motion vector of a temporally neighboring corresponding block, when selectively adding, to a list, a motion vector of each of one or more corresponding blocks each of which is either a block included in a current picture to be coded and spatially neighboring a current block to be coded or a block included in a picture other than the current picture and temporally neighboring the current block, determines whether the second motion vector has a magnitude that is within a predetermined magnitude or not within the predetermined magnitude, and adds the second motion vector to the list when the intra-inter prediction unit determines that the second motion vector has a magnitude that is within the predetermined magnitude range.

A moving picture coding apparatus includes an intra-inter prediction unit which calculates a second motion vector by performing a scaling process on a first motion vector of a temporally neighboring corresponding block, when selectively adding, to a list, a motion vector of each of one or more corresponding blocks each of which is either a block included in a current picture to be coded and spatially neighboring a current block to be coded or a block included in a picture other than the current picture and temporally neighboring the current block, determines whether the second motion vector has a magnitude that is within a predetermined magnitude or not within the predetermined magnitude, and adds the second motion vector to the list when the intra-inter prediction unit determines that the second motion vector has a magnitude that is within the predetermined magnitude range.

An inter prediction method allows a variable coefficient deep learning model to adaptively learn characteristics of a video; transmits a variable coefficient deep learning model parameter generated from the learning from an image encoding device to an image decoding device; and refers to a virtual reference frame generated by the variable coefficient deep learning model.

A three-dimensional data encoding method includes: encoding nodes in an N-ary tree structure of a current three-dimensional point cloud, N being an integer greater than or equal to 2; and generating a bitstream including the nodes encoded. In the encoding: at least one node among the nodes is encoded using an inter prediction mode in which a reference three-dimensional point is referred to; and at least one other node among the nodes is encoded using an intra prediction mode in which the reference three-dimensional point is not referred to, the at least one other node being other than the at least one node. The bitstream further includes information which distinguishes between the at least one other node and the at least one node.

A three-dimensional data encoding method includes: encoding nodes in an N-ary tree structure of a current three-dimensional point cloud, N being an integer greater than or equal to 2; and generating a bitstream including the nodes encoded. In the encoding: at least one node among the nodes is encoded using an inter prediction mode in which a reference three-dimensional point is referred to; and at least one other node among the nodes is encoded using an intra prediction mode in which the reference three-dimensional point is not referred to, the at least one other node being other than the at least one node. The bitstream further includes information which distinguishes between the at least one other node and the at least one node.

One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.

A video processing method includes performing a conversion between a video having one or more video layers including one or more video pictures and a bitstream of the video according to a rule. The rule specifies a condition under which no picture that has been generated by a decoding process for generating an unavailable reference picture is referred to by an active entry in a reference picture list of a current slice of a current picture.