Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.

We address the automatic classification of scanned input media in order to improve color calibration. Since scanner responses vary significantly according to the type of input, a media dependent color calibration for a scanner is desirable for accurately mapping scanner responses to a standard color space. To assist such media dependent calibration, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input medium and the spatial statistics of corresponding images, which may be observed in the scanned images. Adopting two spatial statistical measures of dispersion and periodicity and utilizing extensive training data, we determine well separated decision regions to classify the input medium with a high confidence level. Experimental results over an independent test data set validate the results.

Compressed images may be decompressed for devices using different resolutions. Full decompression and rescaling in the space domain is a very expensive method. We studied downscaled inverses where the image is decompressed partially and a reduced inverse transform is used to recover the image. We studied the design of fast inverses, for a given forward transform. General solutions are presented for M-channel FIR filter banks of which block and lapped transforms are a subset.

We present a novel technique for segmentation of a JPEG-compressed document based on block activity. The activity is measured as the number of bits spent to encode each block. Each number is mapped to a pixel brightness value in an auxiliary image which is then used for segmentation. We introduce the use of such an image and show an example of a simple segmentation algorithm, which was successfully applied to test documents. The desired region can be identified and cropped (or replaced) from the compressed data without decompressing the image.

Compressed images may be decompressed and displayed or printed using different devices at different resolutions. Full decompression and rescaling in space domain is a very expensive method. We studied downscaled inverses where the image is decompressed partially, and a reduced inverse transform is used to recover the image. In this fashion, fewer transform coefficients are used and the synthesis process is simplified. We studied the design of fast inverses, for a given forward transform. General solutions are presented for M-channel finite impulse response (FIR) filterbanks, of which block and lapped transforms are a subset. Designs of faster inverses are presented for popular block and lapped transforms.

We describe a system which automatically annotates images with a set of prespecified keywords, based on supervised color classification of pixels intoNprespecified classes using simple pixelwise operations. The conditional distribution of the chrominance components of pixels belonging to each class is modeled by a two-dimensional Gaussian function, where the mean vector and the covariance matrix for each class are estimated from appropriate training sets. Then, a succession of binary hypothesis tests with image-adaptive thresholds has been employed to decide whether each pixel in a given image belongs to one of the predetermined classes. To this effect, a universal decision threshold is first selected for each class based on receiver operating characteristics (ROC) curves quantifying the optimum “true positive” vs “false positive” performance on the training set. Then, a new method is introduced for adapting these thresholds to the characteristics of individual input images based on histogram cluster analysis. If a particular pixel is found to belong to more than one class, a maximuma posterioriprobability (MAP) rule is employed to resolve the ambiguity. The performance improvement obtained by the proposed adaptive hypothesis testing approach over using universal decision thresholds is demonstrated by annotating a database of 31 images.

Error diffusion is an important technique for digital halftoning. One of the most observable artifacts of error diffusion is the periodically repeating patterns appearing in the areas where the input is constant or slowly varying. We analyze the limit cycle behavior of error diffusion, which explains the periodic oscillation under a constant input. We discuss why certain periodic patterns are more likely to appear than the others in the image. We demonstrate how to increase or reduce the occurrence of a pattern by manipulating the error diffusion weights.>