This application provides a method for predicting a point cloud attribute, an encoder, a decoder, and a storage medium. During point cloud attribute prediction, different selection policies for target adjacent points are designed according to the distribution of repetition points, to determine at least one target adjacent point of a target point, and attribute prediction is performed on the target point according to reconstructed attribute information of the at least one target adjacent point, thereby improving the efficiency and accuracy of point cloud attribute prediction.

Technology for improving coding efficiency by performing a block split suitable for picture coding and decoding is provided. A picture decoding device includes a spatial candidate derivation unit configured to derive a spatial candidate from inter prediction information of a block neighboring a decoding target block and register the derived spatial candidate as a candidate in a first candidate list, a history-based candidate derivation unit configured to generate a second candidate list by adding a history-based candidate included in a history-based candidate list as a candidate to the first candidate list, a candidate selection unit configured to select a selection candidate from candidates included in the second candidate list; and an inter prediction unit configured to perform inter prediction using the selection candidate. The history-based candidate derivation unit switches between whether or not a history-based candidate overlapping a candidate included in the first candidate list is added in accordance with a prediction mode.

The present invention relates to an image processing device and method enabling noise removal to be performed according to images and bit rates. A low-pass filter setting unit 93 sets, from filter coefficients stored in a built-in filter coefficient memory 94, a filter coefficient corresponding to intra prediction mode information and a quantization parameter. A neighboring image setting unit 81 uses the filter coefficient set by the low-pass filter setting unit 93 to subject neighboring pixel values of a current block from frame memory 72 to filtering processing. A prediction image generating unit 82 performs intra prediction using the neighboring pixel values subjected to filtering processing, from the neighboring image setting unit 81, and generates a prediction image. The present invention can be applied to an image encoding device which encodes with the H.264/AVC format, for example.

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 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.

A three-dimensional data encoding method includes: dividing three-dimensional points included in point cloud data into processing units each of which includes one or more of the three-dimensional points; and encoding attribute information of a current three-dimensional point included in a current processing unit, by reference to an encoded processing unit, to generate a bitstream.

The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

There is provided an information processing device and method capable of suppressing a reduction in encoding efficiency. When performing, for attribute information of each point of a point cloud that represents an object having a three-dimensional shape as a set of points, hierarchization of the attribute information by recursively repeating classification of a prediction point for deriving a difference value between the attribute information and a predicted value of the attribute information and a reference point used for deriving the predicted value with respect to the reference point, the reference point is set on the basis of a centroid of points. The present disclosure can be applied to, for example, an information processing device, an image processing device, an encoding device, a decoding device, an electronic device, an information processing method, a program, and the like.

A three-dimensional data encoding method includes: obtaining a plurality of three-dimensional points; generating a prediction tree using the plurality of three-dimensional points; predictive-encoding geometry information of the plurality of three-dimensional points using the prediction tree; and generating a bitstream including encoded data obtained from the predictive-encoding, a total number of three-dimensional points included in the prediction tree, and identification information indicating whether or not to rearrange the plurality of three-dimensional points in Morton order.

Provided is a method for predicting attribute information, a coder, a decoder, and a storage medium. The coder determines a current Morton code corresponding to a point to be predicted in a point cloud to be coded, determines a target Morton code corresponding to the point to be predicted based on the current Morton code and according to a preset neighbor information table, judges whether a neighbor point of the point to be predicted exists in the point cloud to be coded according to the target Morton code, and performs prediction to obtain a predicted attribute value of the point to be predicted according to attribute reconstruction information of the neighbor point in response to that the neighbor point exists in the point cloud to be coded.