Point cloud compression using a space filling curve for level of detail generation

    公开(公告)号:US11754685B2

    公开(公告)日:2023-09-12

    申请号:US17935009

    申请日:2022-09-23

    Applicant: Apple Inc.

    CPC classification number: G01S7/4861 G01S17/42 H03M7/3062 G01S17/89

    Abstract: 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.

    Machine learning video processing systems and methods

    公开(公告)号:US11616960B2

    公开(公告)日:2023-03-28

    申请号:US17210478

    申请日:2021-03-23

    Applicant: Apple Inc.

    Abstract: System and method for improving video encoding and/or video decoding. In embodiments, a video encoding pipeline includes a main encoding pipeline that compresses source image data corresponding with an image frame by processing the source image data based at least in part on encoding parameters to generate encoded image data. Additionally the video encoding pipeline includes a machine learning block communicatively coupled to the main encoding pipeline, in which the machine learning block analyzes content of the image frame by processing the source image data based at least in part on machine learning parameters implemented in the machine learning block when the machine learning block is enabled by the encoding parameters; and the video encoding pipeline adaptively adjusts the encoding parameters based at least in part on the content expected to be present in the image frame to facilitate improving encoding efficiency.

    Encoding and Decoding Visual Content Including Polygonal Meshes

    公开(公告)号:US20230076939A1

    公开(公告)日:2023-03-09

    申请号:US17942032

    申请日:2022-09-09

    Applicant: Apple Inc.

    Abstract: In an example method, a system obtains first data representing a plurality of polygons of a polygon mesh, and performs several operations for each of the polygons, including (i) determining a number of sample points for that polygon, where the number of sample points is determined based on at least one of an area of that polygon or an area of the polygon mesh, (ii) determining a distribution of the sample points for that polygon, and (iii) sampling the polygon mesh in accordance with the determined number of sample points and the determined distribution of sample points, where sampling the polygon mesh includes determining one or more characteristics of the polygon mesh at each of the sample points. The system also outputs second data representing the one or more characteristics of the polygon mesh at one or more of the sample points.

    POINT CLOUD COMPRESSION USING A SPACE FILLING CURVE FOR LEVEL OF DETAIL GENERATION

    公开(公告)号:US20230072818A1

    公开(公告)日:2023-03-09

    申请号:US17935009

    申请日:2022-09-23

    Applicant: Apple Inc.

    Abstract: 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.

    Point cloud compression
    69.
    发明授权

    公开(公告)号:US11527018B2

    公开(公告)日:2022-12-13

    申请号:US17157833

    申请日:2021-01-25

    Applicant: Apple Inc.

    Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.

    SMOOTHED DIRECTIONAL AND DC INTRA PREDICTION

    公开(公告)号:US20220303554A1

    公开(公告)日:2022-09-22

    申请号:US17837846

    申请日:2022-06-10

    Applicant: Apple Inc.

    Abstract: Techniques are disclosed for deriving prediction pixel blocks for use in intra-coding video and combined inter- and intra-coding video. In a first aspect, the techniques may include deriving value(s) for pixel location(s) of the prediction pixel block by, when a prediction direction vector assigned to the prediction vector points to quadrants I or III of a Cartesian plane, deriving the pixel location's value from pixel values in two regions of previously-decoded pixel data intercepted by extending the prediction direction vector in two opposite directions through the pixel location. When the prediction direction vector points toward quadrants II of the Cartesian plane, deriving the pixel location's value from pixel values in one region intercepted by the prediction direction vector through the pixel location, and from a second region intercepted by a vector that is orthogonal to the prediction direction vector.

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