Abstract: Methods and systems for efficient scaling in the transform domain are provided when transform coefficient data is provided as an input to a data processing system, comprising generating a first matrix from transform coefficient data; zeroing out a row or column of, or inserting a row or column of zeros into, the first matrix; generating a second matrix by applying a one-dimensional inverse transform to the first matrix with the zeroed-out at least one row, the at least one column, the inserted at least one row of zeros or the at least one column of zeros; generating a third matrix by regrouping the second matrix; generating a combined matrix by applying a forward transform to the third matrix; and the data processing system scaling the data represented by the transform coefficient data by applying the combined matrix to the transform coefficient data.

Abstract: A method, system, and data structure for the scaling down of data is provided. At least two blocks of transformed data samples representing at least two blocks of original data samples are received. One of at least two tables of constants is selected wherein each table of constants is capable of reducing the number of transformed data samples by a different factor. The constants taken from the selected table are applied to the at least two blocks of transformed data samples to produce one block of transformed data samples representing one block of final data samples. The data is processed one dimension at a time by multiplying the data in one dimension with selected constants taken from previously developed tables corresponding to the desired scale down factor. Scaling down by different factors in each dimension as well as scaling down in one dimension and scaling up in the other dimension may be achieved.

Abstract: A method, system, and data structure for the scaling of data is provided. An exemplary use of the disclosed embodiments involves the scaling up in size or the scaling down in size of computer images. n-dimensional transformed data representing some original n-dimensional real data is received. An m-dimensional inverse transform on the n-dimensional transformed data is performed to produce hybrid data, where 1?m<n. The hybrid data is scaled in p dimensions to produce scaled hybrid data representing a desired p-dimensional change in the n-dimensional real data where p?m. Finally, an m-dimensional forward transform is performed on the scaled hybrid data to produce n-dimensional scaled transformed data.

Abstract: A method and system for efficient scaling in the transform domain, wherein transform coefficient data is provided as an input to a data processing system and scaled in the transform domain by application of a combined matrix. Some embodiments utilize discrete cosine transform data. One embodiment of the invention generates a combined matrix for one-dimensional scaling by selecting a rational scaling factor and matrix dimension value, generating a matrix with some zero values, applying a one-dimensional inverse transform, regrouping, and applying a one-dimensional forward transform. One application of the invention performs up-scaling operations, and another performs down-scaling operations. The invention also provides for two-dimensional scaling by selecting horizontal and vertical scaling parameters and generating first and second combined matrices responsive to the parameters and combining them into a single combined matrix. The invention may also incorporate a predetermined cost function.

Abstract: A method, system, and data structure for the scaling down of data is provided. At least two blocks of transformed data samples representing at least two blocks of original data samples are received. One of at least two tables of constants is selected wherein each table of constants is capable of reducing the number of transformed data samples by a different factor. The constants taken from the selected table are applied to the at least two blocks of transformed data samples to produce one block of transformed data samples representing one block of final data samples. The data is processed one dimension at a time by multiplying the data in one dimension with selected constants taken from previously developed tables corresponding to the desired scale down factor. Scaling down by different factors in each dimension as well as scaling down in one dimension and scaling up in the other dimension may be achieved.