CONNECTIVITY CODING FOR SYMMETRY MESH
    9.
    发明公开

    公开(公告)号:US20240127490A1

    公开(公告)日:2024-04-18

    申请号:US18312722

    申请日:2023-05-05

    摘要: A method of encoding includes receiving a polygon mesh comprising a plurality of faces and a plurality vertices; separating the polygon mesh into a left side half mesh and a right side half mesh by a plane; extracting the left side half mesh comprising a first plurality of vertices in-plane and on a left side of the polygon mesh; remapping the plurality of faces to represent a new vertices order in order to generate a plurality of remapped faces; extracting the plurality of remapped faces; compressing the left side half mesh by a codec; predicting a location of a second plurality of vertices on the right side half mesh using a symmetry based prediction on the compressed left side half mesh; and connecting the left side half mesh and the right side half mesh.

    Image compression sampling method and assembly

    公开(公告)号:US11936869B2

    公开(公告)日:2024-03-19

    申请号:US18246470

    申请日:2021-07-30

    摘要: An image compression sampling method and assembly are provided. The method includes: performing sparse representation on a target image by using an initial sparse matrix, quantifying an initial sparse representation result to obtain an optimized sparse representation result, and obtaining an optimized sparse matrix; constructing a product matrix by using the optimized sparse matrix and an initial measurement matrix, and adjusting absolute values of off-diagonal elements in the product matrix to be less than a correlation threshold; performing singular value decomposition on the product matrix to obtain a diagonal matrix and a left singular matrix, and updating the diagonal matrix according to a quantity of samplings of the initial measurement matrix; and optimizing the initial measurement matrix by using the left singular matrix and the updated diagonal matrix to obtain an optimized measurement matrix, and collecting image data by using the optimized sparse matrix and the optimized measurement matrix.