Abstract: Auto exposure processing for spherical images improves image quality by reducing visible exposure level variation along a stitch line within a spherical image. An average global luminance value is determined based on luminance values determined for first and second images, which are based on auto exposure configurations of first and second image sensors used to obtain those first and second images. Delta luminance values are determined for the first and second images using the average global luminance value. The first and second images are then updated using the delta luminance values, and the updated first and second images are used to produce a spherical image.

Abstract: Auto exposure processing for spherical images improves image quality by reducing visible exposure level variation along a stitch line within a spherical image. An average global luminance value is determined based on auto exposure configurations of first and second image sensors. Delta luminance values are determined for each of the image sensors based on the average global luminance value and a luminance variance between the image sensors. The auto exposure configurations of the image sensors are then updated using the delta luminance values, and the updated auto exposure configurations are used to capture images which are then combined to produce the spherical image. In some cases wherein updating the auto exposure configurations using the delta luminance values would breach a threshold representing a target luminosity for the scene, the use of the delta luminance values may be limited or those values may instead be discarded.

Abstract: Auto exposure metering is adapted for spherical panoramic content. Using input image data, a first metering map is generated for a selected image sensor and a second metering map is generated for an unselected image sensor. Auto exposure level values for the selected image sensor and for the unselected image sensor are respectively metered using the first metering map and the second metering map, such as by adjusting luminance weights in certain locations of the respective image sensor panoramic image capture band. Hemispherical images are processed using the auto exposure metered level values and stitched together in a panoramic format to produce a spherical panoramic image. The metering maps are generated to account for areas of greatest image data importance relative to a primary orientation direction of the spherical panoramic image. This allows for effective auto exposure metering of such areas within the resulting spherical panoramic image.

Abstract: Optical center calibration may include obtaining one or more parameters for optical center calibration, obtaining an input image captured by an image capture device using a lens, and determining a calibration circle using the parameters and the input image. Determining the calibration circle may include extracting rays using the input image, estimating contours using the input image and the rays, and estimating the calibration circle using the input image and the contours. The calibration may be iteratively improved by repeating calibration based on the input image and a previous iteration of optical center calibration.

Abstract: Auto exposure processing for spherical images improves image quality by reducing visible exposure level variation along a stitch line within a spherical image. An average global luminance value is determined based on auto exposure configurations of first and second image sensors. Delta luminance values are determined for each of the image sensors based on the average global luminance value and a luminance variance between the image sensors. The auto exposure configurations of the image sensors are then updated using the delta luminance values, and the updated auto exposure configurations are used to capture images which are then combined to produce the spherical image. In some cases wherein updating the auto exposure configurations using the delta luminance values would breach a threshold representing a target luminosity for the scene, the use of the delta luminance values may be limited or those values may instead be discarded.

Abstract: Auto exposure metering is adapted for spherical panoramic content. Using input image data, a first metering map is generated for a selected image sensor and a second metering map is generated for an unselected image sensor. Auto exposure level values for the selected image sensor and for the unselected image sensor are respectively metered using the first metering map and the second metering map, such as by adjusting luminance weights in certain locations of the respective image sensor panoramic image capture band. Hemispherical images are processed using the auto exposure metered level values and stitched together in a panoramic format to produce a spherical panoramic image. The metering maps are generated to account for areas of greatest image data importance relative to a primary orientation direction of the spherical panoramic image. This allows for effective auto exposure metering of such areas within the resulting spherical panoramic image.

Abstract: Auto exposure processing for spherical images improves image quality by reducing visible exposure level variation along a stitch line within a spherical image. An average global luminance value is determined based on auto exposure configurations of first and second image sensors. Delta luminance values are determined for each of the image sensors based on the average global luminance value and a luminance variance between the image sensors. The auto exposure configurations of the image sensors are then updated using the delta luminance values, and the updated auto exposure configurations are used to capture images which are then combined to produce the spherical image. In some cases wherein updating the auto exposure configurations using the delta luminance values would breach a threshold representing a target luminosity for the scene, the use of the delta luminance values may be limited or those values may instead be discarded.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Optical center calibration may include obtaining one or more parameters for optical center calibration, obtaining an input image captured by an image capture device using a lens, and determining a calibration circle using the parameters and the input image. Determining the calibration circle may include extracting rays using the input image, estimating contours using the input image and the rays, and estimating the calibration circle using the input image and the contours. The calibration may be iteratively improved by repeating calibration based on the input image and a previous iteration of optical center calibration.

Abstract: Auto exposure metering is adapted for spherical panoramic content. Using input image data, a first metering map is generated for a selected image sensor and a second metering map is generated for an unselected image sensor. Auto exposure level values for the selected image sensor and for the unselected image sensor are respectively metered using the first metering map and the second metering map, such as by adjusting luminance weights in certain locations of the respective image sensor panoramic image capture band. Hemispherical images are processed using the auto exposure metered level values and stitched together in a panoramic format to produce a spherical panoramic image. The metering maps are generated to account for areas of greatest image data importance relative to a primary orientation direction of the spherical panoramic image. This allows for effective auto exposure metering of such areas within the resulting spherical panoramic image.

Abstract: Auto exposure metering is adapted for spherical panoramic content. Using input image data, a first metering map is generated for a selected image sensor and a second metering map is generated for an unselected image sensor. Auto exposure level values for the selected image sensor and for the unselected image sensor are respectively metered using the first metering map and the second metering map, such as by adjusting luminance weights in certain locations of the respective image sensor panoramic image capture band. Hemispherical images are processed using the auto exposure metered level values and stitched together in a panoramic format to produce a spherical panoramic image. The metering maps are generated to account for areas of greatest image data importance relative to a primary orientation direction of the spherical panoramic image. This allows for effective auto exposure metering of such areas within the resulting spherical panoramic image.

Abstract: Auto exposure processing for spherical images improves image quality by reducing visible exposure level variation along a stitch line within a spherical image. An average global luminance value is determined based on auto exposure configurations of first and second image sensors. Delta luminance values are determined for each of the image sensors based on the average global luminance value and a luminance variance between the image sensors. The auto exposure configurations of the image sensors are then updated using the delta luminance values, and the updated auto exposure configurations are used to capture images which are then combined to produce the spherical image. In some cases wherein updating the auto exposure configurations using the delta luminance values would breach a threshold representing a target luminosity for the scene, the use of the delta luminance values may be limited or those values may instead be discarded.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Color correction is integrated within global tone mapping operations to modify an image captured using an image capture device. New luminance values are determined for the pixels of the image by performing global tone mapping against those pixels using one or more sets of color correction values, which are applied against respective luminance values and color components of those pixels. The sets of color correction values are identified within 3×3 color correction matrices. A gain curve for modifying contrast values of the image is determined based on at least one of the new luminance values. A modified image is then generated by modifying the contrast values according to the gain curve, for example, by applying a gamma curve against the contrast values using data stored in a three-dimensional lookup table.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Color correction is integrated within global tone mapping operations to modify an image captured using an image capture device. New luminance values are determined for the pixels of the image by performing global tone mapping against those pixels using one or more sets of color correction values, which are applied against respective luminance values and color components of those pixels. The sets of color correction values are identified within 3×3 color correction matrices. A gain curve for modifying contrast values of the image is determined based on at least one of the new luminance values. A modified image is then generated by modifying the contrast values according to the gain curve, for example, by applying a gamma curve against the contrast values using data stored in a three-dimensional lookup table.