Assuming a Laplacian distribution, there exists a well known method for optimally biasing the reconstruction levels for the quantized ac discrete cosine transform (DCT) coefficients in the JPEG decoder. This, however, requires an estimate of the Laplacian distribution parameter. We derive a new, maximum likelihood estimate of the Laplacian parameter using only the quantized coefficients available at the decoder. We quantify the benefits of biased reconstruction through extensive simulations and demonstrate that such improvements are very close to the best possible resulting from centroid reconstruction.

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The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. Experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface are presented. For illumination our system is composed of a mercury arc lamp coupled to a 10nm band-pass spectrometer. A structured lighting system is also coupled to our multispectral system in order to provide 3D information of the patient arm orientation. Images of each patient arm are captured under every possible combinations of illuminants and the optimal combination of wavelengths for a given subject to maximize vein contrast using linear discriminant analysis is determined.

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