Abstract: Techniques are disclosed involving contrast adjustment for images. For example, an input image is classified based on its pixel value characteristics, as expressed in an input brightness histogram. From such a classification, a target histogram distribution for a corresponding output image (i.e., a contrast-adjusted transformation of the input image) may be specified. With the target histogram of the output image specified, a transformation function may be derived that maps input image values to output image values. Moreover, transitions of such transformation functions may be smoothed. Such smoothing may provide advantages, such as a reduction in flickering associated with video data.

Abstract: System, method, and computer program product to adaptively blend the interpolation results from an 8-tap Lanczos filter and the interpolation results from a bilinear filter, according to the local transitions of the input content. Artifacts may occur, which may be identified as such and corrected. Pixels that represent artifacts in the blended image may be replaced with the pixel for that location taken from the bilinear interpolation.

Abstract: Methods and systems to manipulate color processing parameters to allow the detection of an arbitrary color of interest. Such reconfigurations may enable general point-of-interest color processing. Color mapping curves may also be configured, to accomplish the tasks of color correction, enhancement, de-saturation, and color compression.

Abstract: A method is provided for performing a classification. The method includes ranking a plurality of features of a training set according to how closely they are correlated to their corresponding classifications, extracting a plurality of features of from input data, selecting a subset of the plurality of features such that a computational resource cost of the subset is less than a predefined computational resource maximum and a degree of utility achieved by a classification of the subset by a selected classifier is optimized and exceeds a predefined utility minimum, predicting one of the features of the sensor data that is not selected for the subset of features from a predefined number of past samples of the feature and adding the predicted feature to the subset of features, and classifying, by a processor, using the selected classifier and the resulting subset of features.

Abstract: System, method, and computer program product to adaptively blend the interpolation results from an 8-tap Lanczos filter and the interpolation results from a bilinear filter, according to the local transitions of the input content. Artifacts may occur, which may be identified as such and corrected. Pixels that represent artifacts in the blended image may be replaced with the pixel for that location taken from the bilinear interpolation.

Abstract: Techniques are described that can be used to either compress or expand video. Color compression techniques are described that can be used to compress the wide color gamut content into lower color gamut for inclusion in a baseline layer. Color expansion techniques are described that convert lower color gamut data into wider color gamut format for inclusion in an enhancement layer. Both of the baseline video stream and enhancement layer video streams may be transmitted through a channel or stored in a memory device to be viewed later. Accordingly, both baseline and enhancement video layers are available so that either lower or higher quality displays can be used to display video.

Abstract: A method is provided for selecting features for classification that trades classification efficiency for computational resources. The method includes ranking a plurality of features of a training set according to how closely they are correlated to their corresponding classifications, receiving sensor data including a plurality of features, and selecting a subset of the features of the sensor data, according to the ranking of the features of the training data such that a computational resource cost of the subset is less than a predefined computational resource maximum and the degree of utility achieved by a classification of the subset of features by a selected classifier is optimized and exceeds a predefined utility minimum.