Abstract: 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 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.

Abstract: 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: 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: 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?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 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: 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?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 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: 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 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: 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.

Abstract: 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.

Abstract: 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.

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 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.

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: 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.

Abstract: 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.

Abstract: 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.