International Business Machines Corporation

Infoprintsolutions Company, LLC

Timothy James Trenary

Joan LaVerne Mitchell

Nenad Rijavec

Ian R Finlay

Marco Martens

Tomasz J Nowicki

Jennifer Quirin Trelewicz

Joan La Verne Mitchell

Michael T Brady

Charles A Micchelli

Yue Qiao

John B Condon

Danielle Kathryn Dittrich

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.

System and computer readable medium for the scaling down of data

A halftone method and apparatus provides a line screen frequency of N/2 for a printer resolution of N without negative print effects. A 300 lines per inch halftone screen for a 600 dpi printer may thus be created by alternating white and saturated colored lines when half of a given color component's pels are on. Empirical rules are used to create the screens based on how that printer creates consistent and reliable levels for each color component. These rules allow intermediate intensity values between white and full-on at each pel. Since the halftoning is done in the hardware just before printing, the print direction relative to the threshold matrix is known. No rotation capability is needed in the hardware. If an image needs to be rotated, it can be done previously to being sent to the hardware. Furthermore, because a pair of pels are turned on for the lightest values (or possibly with a one level difference between the first and second dots in the pair), the electronics are stressed less. Moreover, the drive will be at a lower frequency than the first dot in the traditional halftone cell, which remains isolated for many levels.

Halftone method and apparatus that provides a line screen frequency of N/2 for a printer resolution of N dots per inch without negative print effects

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. In addition, the de-quantization of the quantized transform coefficients may be accomplished by pre-multiplication of the selected constants when the quantization values are known. In a similar way the re-quantization may be accomplished by a pre-division of the selected constants. Both de-quantization and re-quantization may be combined when the input quantized transform coefficients and output quantized transform coefficients are desired.

Scaling down of data

At least a 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&#x2266;m&#x2266;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&#x2266;m. Finally, an m-dimensional forward transform is performed on the scaled hybrid data to produce n-dimensional scaled transformed data.

Scaling of multi-dimensional data in a hybrid domain

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.

Efficient scaling in transform domain

A method 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. In addition, the de-quantization of the quantized transform coefficients may be accomplished by pre-multiplication of the selected constants when the quantization values are known. In a similar way the re-quantization may be accomplished by a pre-division of the selected constants. Both de-quantization and re-quantization may be combined when the input quantized transform coefficients and output quantized transform coefficients are desired.

Method for the scaling down of data

A method 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&#x2266;m&#x3c;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&#x2266;m. Finally, an m-dimensional forward transform is performed on the scaled hybrid data to produce n-dimensional scaled transformed data.

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. In addition, the de-quantization of the quantized transform coefficients may be accomplished by pre-multiplication of the selected constants when the quantization values are known. In a similar way the re-quantization may be accomplished by a pre-division of the selected constants. Both de-quantization and re-quantization may be combined when the input quantized transform coefficients and output quantized transform coefficients are desired.

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.

Provided are a method, system and program for fractionally shifting input data subject to a first transform process without first applying an inverse transformation by using a second transform process. The second transform process applies a transformed matrix to the transformed data. At least one transformed matrix is provided in non-volatile storage, wherein each transformed matrix is generated by applying the first transform process to a fractional shift matrix operator. The input data that was transformed using the first transform process is received and the at least one transformed matrix is applied to the transformed input data to generate transformed data that represents fractionally shifted transformed output data.

Method, system, and program for fractionally shifting data subject to a previous transformation

We present a communication system which enables two or more parties to secretly communicate through an existing digital channel which has a primary function other than this secret communication. A first party receives a series of cover data sets, hides a certain amount of auxiliary data in the cover data sets, and then relays these cover data sets containing hidden data to a second party, aware of the hidden data. This second party may then extract the hidden data and either restore it to its original state (the state it was in before the first party received it) and send it along to its original intended destination, or may just simply extract the hidden auxiliary data. There exist a plethora of techniques for hiding auxiliary data in cover data, and any of these can be used for the hiding phase of the system. For example, in a JPEG cover data set, a Huffman table may be modified in such a way as to have no impact on the observable nature of the image, and several such schemes are presented here. Since there are so many ways in which to exploit a particular cover data set for secret communication, it is necessary that the first and second parties have pre-established a set of rules by which they will communicate. There must be agreement on the hiding technique, cover data type and location of the hidden data within the cover.

System and method for secret communication

A method for scaling a signal sample rate includes interpolating between at least two scaling ratios to calculate an arbitrary scaling ratio, using a predetermined interpolation algorithm, and scaling a sample rate for a first portion of the signal using a first scaling ratio, and scaling a sample rate for a second portion of the signal using a second scaling ratio, to form a scaled signal having an average scaling ratio equal to the arbitrary scaling ratio.

Method and system for scaling a signal sample rate

Method and apparatus for the scaling down of data

A method, system, and data structure for the scaling up of data is provided. A block of transformed data samples is received wherein the transformed data samples represent original data samples. One of at least two tables of constants is selected wherein each table of constants is capable of increasing the number of data samples by a different factor. The constants taken from the selected table are applied to the block of transformed data samples to produce at least two blocks of transformed data samples representing at least two blocks of original 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 up factor. Scaling up by different factors in each dimension as well as scaling down in one dimension and scaling up in the other dimension may be achieved. In addition, the de-quantization of the quantized transform coefficients may be accomplished by pre-multiplication of the scaling constants when the quantization values are known. In a similar way the re-quantization may be accomplished by a pre-divsion of the scaling constants. Both de-quantization and re-quantization may be combined when the input quantized transform coefficients and output quantized transform coefficients are desired.

Method and apparatus for the scaling up of data

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&#x2266;m&#x3c;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&#x2266;m. Finally, an m-dimensional forward transform is performed on the scaled hybrid data to produce n-dimensional scaled transformed data.

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Fort Collins, CO, United States of America

Timothy James Trenary