Abstract: An electronic database for image interpolation is generated by a computer. The computer generates a low-resolution image from a training image, a plurality of representative vectors from the low-resolution image, and a plurality of interpolation filters corresponding to each of the representative vectors. The interpolation filters and the representative vectors are generated off-line and can be used to perform image interpolation on an image other than the training image. The database can be stored in a device such as computer or a printer.

Abstract: An electronic database for image interpolation is generated by a computer. The computer generates a low-resolution image from a training image, a plurality of representative vectors from the low-resolution image, and a plurality of interpolation filters corresponding to each of the representative vectors. The interpolation filters and the representative vectors are generated off-line and can be used to perform image interpolation on an image other than the training image. The database can be stored in a device such as computer or a printer.

Abstract: An electronic database for image interpolation is generated by a computer. The computer generates a low-resolution image from a training image, a plurality of representative vectors from the low-resolution image, and a plurality of interpolation filters corresponding to each of the representative vectors. The interpolation filters and the representative vectors are generated off-line and can be used to perform image interpolation on an image other than the training image. The database can be stored in a device such as computer or a printer.

Abstract: A data resolution synthesis algorithm takes low resolution source input data (e.g., data degraded from compression or data acquired from low quality imaging devices) to synthesize high resolution output data. In an exemplary embodiment for color printing, the algorithm is performed by characterizing a multi-pixel area, or window, around a pixel that can benefit from resolution enhancement. To interpolate to a high resolution output a set of spatial filters is applied to the data area based on the window characterization. The output of the resolution synthesizer is a set of multiple pixels for each input pixel, representing the source input pixel in a higher resolution enhanced version. The filters are chosen from a stored data base (generic or specifically applicable data base for each type input device) created to fit input/output device requirements.

Abstract: A data resolution synthesis algorithm takes low resolution source input data (e.g., data degraded from compression or data acquired from low quality imaging devices) to synthesize high resolution output data. In an exemplary embodiment for color printing, the algorithm is performed by characterizing a multi-pixel area, or window, around a pixel that can benefit from resolution enhancement. To interpolate to a high resolution output a set of spatial filters is applied to the data area based on the window characterization. The output of the resolution synthesizer is a set of multiple pixels for each input pixel, representing the source input pixel in a higher resolution enhanced version. The filters are chosen from a stored data base (generic or specifically applicable data base for each type input device) created to fit input/output device requirements.

Abstract: A digital color image quantization mechanism employs sequential product code vector quantization, to sequentially extract chrominance and luminance values from the vectors and quantizes chrominance and luminance features based upon a conditional distribution of these features within partitioned regions of chrominance/luminance color space. The mechanism sequentially partitions a histogram of the original digital color image in luminance, chrominance (Y,Cb,Cr) space coordinates into a plurality of sub-regions or color space cells, such that each partitioned color cell is associated with a color of the output palette through which the color composition of a reproduced color image is defined. A splitting criterion determines the sequential order of partitioning of an axis.

Abstract: A digital color image quantization mechanism employs sequential product code vector quantization, to sequentially extract chrominance and luminance values from the vectors and quantizes chrominance and luminance features in accordance with an asymptotically optimal quantizer density function of the color space histogram along respective chrominance and luminance axes into a plurality of luminance-chrominance sub-regions or color space cells, such that each partitioned color cell is associated with a color of the output palette through which the color composition of a reproduced color image is defined. A map of chrominance and luminance output codes is generated for the respective pixels of the output color image in accordance with the axial splitting or quantization of the chrominance and luminance components of the histogram.

Abstract: A method for creating from a digitized color image, a tree structured partitioning of all pixels in the image into M disjoint sets, with the pixels in each set having similar color values, the method comprising the steps of: separating and assigning all pixels in the image into disjoint sets so as to minimize the difference between the respective pixel color values in each set; selecting one of the sets which has a greatest variation of color values of assigned pixels, and separating and assigning all pixels in the selected set into new, disjoint sets, with the pixels assigned so as to minimize the differences between their respective color values; continuing the selecting, separating and assigning steps for sets until a total of M sets have been derived; and then assigning to all pixels in each set, a mean color value calculated from all pixel colors in the set.