Abstract: A method, an apparatus, an electronic device, and a computer storage medium for accessing raster data are provided. The raster data access method comprises: executing a corresponding window query input to determine a target pixel resolution corresponding to a query window according to a raster data access request; calculating a sampling level according to the target pixel resolution and a stored pixel resolution of a raster object, and determining whether the sampling level is greater than a threshold; and accessing raster data corresponding to the query window according to a column-level index if the sampling level is greater than the threshold, the column-level index being an index generated by down-sampling according to raster data of the raster object in a column. Using the raster data access method can achieve quick access to raster data.

Abstract: Generating a table with at least one skewed row, skewed column, shifted row, or shifted column is described. A table generation system generates a table that includes cells arranged in a grid comprising rows and columns, and defines each cell using a grid address, a grid span, a grid angle, a string skew value, a string shift value, and a shift indicator for the cell. The table generation system may receive input modifying a grid angle for at least one row or column and generate a modified table by skewing cells included in the at least one row or column by the grid angle. The table generation system may additionally or alternatively receive input shifting at least one row or column by a string shift value and modify the display of the table by shifting the at least one row or column according to the string shift value.

Abstract: A method for predicting an image is provided. The method include (i) determining prediction parameters of a current block in a bitstream; (ii) determining a matrix-based intra prediction (MIP) input sample of the current block based on neighboring samples of the current block and the prediction parameters; (iii) setting a shifting number parameter (sW) as a first fixed value; (iv) setting a shifting offset parameter (fO) as a second fixed value; and (v) determining an MIP prediction sample of the current block based on an MIP weighting matrix, the MIP input sample, the shifting offset parameter (fO), and the shifting number parameter (sW).

Abstract: Objectness indicating a degree of accuracy of a single object is accurately estimated. An edge detection unit 30 detects an edge for a depth image, an edge density/uniformity calculation unit 40 calculates an edge density on the periphery of a candidate region, an edge density inside the candidate region, and edge uniformity on the periphery of the candidate region. An objectness calculation unit 42 calculates the objectness of the candidate region based on the edge density on the periphery of the candidate region, the edge density inside the candidate region, and the edge uniformity on the periphery of the candidate region.

Abstract: An auto-focusing method includes dividing an image into a plurality of blocks, performing discrete cosine transform (DCT) on image data of the plurality of blocks to output a plurality of DCT blocks each comprising DCT coefficients, generating a DCT mask for selecting DCT coefficients corresponding to a selected frequency range in a DCT block among the plurality of DCT blocks, and calculating a focus value of the image by applying the generated DCT mask to the DCT block.