Abstract: Methods and apparatuses are disclosed to personalize image capture operations of imaging equipment according to models that correspond uniquely to subjects being imaged. According to these techniques, a subject's face may be detected from a first image supplied by an image source and a first model of the subject may be developed from the detected face. The first model of the subject may be compared to another model of the subject developed from other images. Image adjustment parameters may be generated from a comparison of these models, which may control image adjustment techniques that are applied to the newly captured image of the subject. In this manner, aspects of the present disclosure may generate image capture operations that are tailored to characteristics of the subjects being imaged and avoid artifacts that otherwise could cause image degradation.

Abstract: Methods and apparatuses are disclosed to personalize image capture operations of imaging equipment according to models that correspond uniquely to subjects being imaged. According to these techniques, a subject's face may be detected from a first image supplied by an image source and a first model of the subject may be developed from the detected face. The first model of the subject may be compared to another model of the subject developed from other images. Image adjustment parameters may be generated from a comparison of these models, which may control image adjustment techniques that are applied to the newly captured image of the subject. In this manner, aspects of the present disclosure may generate image capture operations that are tailored to characteristics of the subjects being imaged and avoid artifacts that otherwise could cause image degradation.

Abstract: A method includes identifying feature points in an image in images generated of a scene by a camera and identifying locations of the identified feature points in remaining images in the images. The method also includes selecting a group of the identified feature points indicative of relative motion of the camera between image captures and aligning a set of the images using the selected group of feature points. The method may further include selecting a reference image from the set of aligned images, weighting other images the set, and combining the reference image with the weighted images. Weighting of the other images may include, for each other image in the set, comparing the other image and the reference image to identify one or more moving objects in the other image and applying a weight to pixel locations in the other image.

Abstract: A mobile device includes an embedded digital camera that is configured to capture a burst of N images. The mobile device includes processing circuitry comprising a registration module configured to, for each image within the burst of images: analyze an amount of warp of the image and generate a set of affine matrices indicating the amount of warp of the image. The processing circuitry includes a High Fidelity Interpolation block configured to, for each image within the burst of images: perform affine transformation using the set of affine matrices associated with the image, apply an aliasing-retaining interpolation filter, and implement rotation transformation and sub-pixel shifts, yielding an interpolated image. The processing circuitry includes a blending module configured to receive the N interpolated images and blend the N interpolated images into a single-blended image having a user-selected digital zoom ratio.

Abstract: A mobile device includes an embedded digital camera that is configured to capture a burst of N images. The mobile device includes processing circuitry comprising a registration module configured to, for each image within the burst of images: analyze an amount of warp of the image and generate a set of affine matrices indicating the amount of warp of the image. The processing circuitry includes a High Fidelity Interpolation block configured to, for each image within the burst of images: perform affine transformation using the set of affine matrices associated with the image, apply an aliasing-retaining interpolation filter, and implement rotation transformation and sub-pixel shifts, yielding an interpolated image. The processing circuitry includes a blending module configured to receive the N interpolated images and blend the N interpolated images into a single-blended image having a user-selected digital zoom ratio.

Abstract: A method and apparatus for aligning and combining images. The method includes identifying feature points in an image in images generated of a scene by a camera and identifying locations of the identified feature points in remaining images in the images. The method also includes selecting a group of the identified feature points indicative of relative motion of the camera between image captures and aligning a set of the images using the selected group of feature points. The method may further include selecting a reference image from the set of aligned images, weighting other images the set, and combining the reference image with the weighted images. Weighting of the other images may include, for each other image in the set, comparing the other image and the reference image to identify one or more moving objects in the other image and applying a weight to pixel locations in the other image.

Abstract: A video encoded as a bit stream is decoded using trained sparse orthonormal transforms generated from decoded prediction residual signals, wherein the transforms have associated indices. A current macroblock is entropy decoded and inverse quantized to produce decoded coefficients. For the current macroblock, an L0-norm regularized training process generates a set of mode-dependent sparse orthonormal inverse transforms, selected according to a prediction mode signaled in the bit stream. Decoded coefficients are inverse transformed and then are combined with previously decoded macroblocks to generate an output macroblock of a reconstructed video.

Abstract: A method and apparatus is disclosed herein for performing compression with sparse orthonormal transforms. One method includes receiving a block of data; classifying the block of data based on directional structure; selecting one of a plurality of available directional, orthonormal transforms to apply to the block based on results of classifying the block; and applying the selected transform to the block to generate a plurality of coefficients, thereby producing a compressed version of the block.

Abstract: A video encoded as a bit stream is decoded using trained sparse orthonormal transforms generated from decoded prediction residual signals, wherein the transforms have associated indices. A current macroblock is entropy decoded and inverse quantized to produce decoded coefficients. For the current macroblock, an L0-norm regularized training process generates a set of mode-dependent sparse orthonormal inverse transforms, selected according to a prediction mode signaled in the bit stream. Decoded coefficients are inverse transformed and then are combined with previously decoded macroblocks to generate an output macroblock of a reconstructed video.

Abstract: A method and apparatus for selecting a reference frame for producing an encoded image. The method includes retrieving a histogram for a current frame, determining the difference between the histogram and a previous histogram, and calculating adaptive threshold utilizing the determined difference and encoding the frame as intra frame if it is an intra frame, and selecting a reference frame and encoding the frame as non-intra frame if the frame is a non-intra frame.

Abstract: A method and apparatus is disclosed herein for performing compression with sparse orthonormal transforms. In one embodiment, the method comprises receiving a block of data; classifying the block of data based on directional structure; selecting one of a plurality of available directional, orthonormal transforms to apply to the block based on results of classifying the block; and applying the selected transform to the block to generate a plurality of coefficients, thereby producing a compressed version of the block.