Abstract: A method and system for tone-mapping an image includes determining a tone-curve based on a model of image contrast distortion between the input image and a tone-mapped image and tone-mapping the input image according to the determined tone-curve. Determining the tone curve includes analytically calculating values of the tone-curve that reduce the image contrast distortion within the model of image contrast distortion. A tone-mapping operator includes a noise model generator and a tone-mapping module operable to receive one or more contextual parameters. The tone-mapping module includes an edge-stopping filtering submodule for extracting a base layer and detail layer of the input image, a tone-curve generating submodule and a combining submodule for combining the base layer and detail layer. At least one of the edge-stopping filtering submodule, the tone-curve generating submodule and combining submodule is adjustable based on the contextual parameters.

Abstract: A method and system for tone-mapping an image includes determining a tone-curve based on a model of image contrast distortion between the input image and a tone-mapped image and tone-mapping the input image according to the determined tone-curve. Determining the tone curve includes analytically calculating values of the tone-curve that reduce the image contrast distortion within the model of image contrast distortion. A tone-mapping operator includes a noise model generator and a tone-mapping module operable to receive one or more contextual parameters. The tone-mapping module includes an edge-stopping filtering submodule for extracting a base layer and detail layer of the input image, a tone-curve generating submodule and a combining submodule for combining the base layer and detail layer. At least one of the edge-stopping filtering submodule, the tone-curve generating submodule and combining submodule is adjustable based on the contextual parameters.

Abstract: Embodiments of this application provide a method for processing image signal conversion, including: obtaining a primary color component of a to-be-processed pixel; and converting a first value of the primary color component into a second value based on a preset dynamic range conversion model, where the dynamic range conversion model is: L ? = F ? ( L ) = a × ( p × L n ( k 1 × p - k 2 ) × L n + k 3 ) m + b , where L is the first value, L? is the second value, and k1, k2, k3, a, b, m, n, and p are model parameters.

Abstract: A method and system for tone-mapping an image includes determining a tone-curve based on a model of image contrast distortion between the input image and a tone-mapped image and tone-mapping the input image according to the determined tone-curve. Determining the tone curve includes analytically calculating values of the tone-curve that reduce the image contrast distortion within the model of image contrast distortion. A tone-mapping operator includes a noise model generator and a tone-mapping module operable to receive one or more contextual parameters. The tone-mapping module includes an edge-stopping filtering submodule for extracting a base layer and detail layer of the input image, a tone-curve generating submodule and a combining submodule for combining the base layer and detail layer. At least one of the edge-stopping filtering submodule, the tone-curve generating submodule and combining submodule is adjustable based on the contextual parameters.

Abstract: A method and system for tone-mapping an image includes determining a tone-curve based on a model of image contrast distortion between the input image and a tone-mapped image and tone-mapping the input image according to the determined tone-curve. Determining the tone curve includes analytically calculating values of the tone-curve that reduce the image contrast distortion within the model of image contrast distortion. A tone-mapping operator includes a noise model generator and a tone-mapping module operable to receive one or more contextual parameters. The tone-mapping module includes an edge-stopping filtering submodule for extracting a base layer and detail layer of the input image, a tone-curve generating submodule and a combining submodule for combining the base layer and detail layer. At least one of the edge-stopping filtering submodule, the tone-curve generating submodule and combining submodule is adjustable based on the contextual parameters.

Abstract: The following provides a system and method to display images on a display screen (e.g. via a display panel or by projection) and the processing of image data therefor. In one aspect, the system implements a luminance retargeting method for altering the perceived contrast and/or colours of an image to match their appearance under different luminance levels. In another aspect, the system may provide a method for transforming an image locally within a sub-area of an image to adjust image contrast for display by a display device. In yet another aspect, the system may provide a method for transforming an image of a first luminance to adjust a perceived colour hue thereof for display by a display device according to a second luminance. In yet another aspect, the system may provide a method for transforming an image having a first luminance to adjust a colour saturation thereof for display by a display device having a second luminance.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: The following provides a system and method to display images on a display screen (e.g. via a display panel or by projection) and the processing of image data therefor. In one aspect, the system implements a luminance retargeting method for altering the perceived contrast and/or colours of an image to match their appearance under different luminance levels. In another aspect, the system may provide a method for transforming an image locally within a sub-area of an image to adjust image contrast for display by a display device. In yet another aspect, the system may provide a method for transforming an image of a first luminance to adjust a perceived colour hue thereof for display by a display device according to a second luminance. In yet another aspect, the system may provide a method for transforming an image having a first luminance to adjust a colour saturation thereof for display by a display device having a second luminance.

Abstract: A method and system for tone-mapping an image includes determining a tone-curve based on a model of image contrast distortion between the input image and a tone-mapped image and tone-mapping the input image according to the determined tone-curve. Determining the tone curve includes analytically calculating values of the tone-curve that reduce the image contrast distortion within the model of image contrast distortion. A tone-mapping operator includes a noise model generator and a tone-mapping module operable to receive one or more contextual parameters. The tone-mapping module includes an edge-stopping filtering submodule for extracting a base layer and detail layer of the input image, a tone-curve generating submodule and a combining submodule for combining the base layer and detail layer. At least one of the edge-stopping filtering submodule, the tone-curve generating submodule and combining submodule is adjustable based on the contextual parameters.

Abstract: The following provides a system and method to display images on a display screen (e.g. via a display panel or by projection) and the processing of image data therefor. In one aspect, the system implements a luminance retargeting method for altering the perceived contrast and/or colours of an image to match their appearance under different luminance levels. In another aspect, the system may provide a method for transforming an image locally within a sub-area of an image to adjust image contrast for display by a display device. In yet another aspect, the system may provide a method for transforming an image of a first luminance to adjust a perceived colour hue thereof for display by a display device according to a second luminance. In yet another aspect, the system may provide a method for transforming an image having a first luminance to adjust a colour saturation thereof for display by a display device having a second luminance.

Abstract: The following provides a system and method to display images on a display screen (e.g. via a display panel or by projection) and the processing of image data therefor. In one aspect, the system implements a luminance retargeting method for altering the perceived contrast and/or colors of an image to match their appearance under different luminance levels. In another aspect, the system may provide a method for transforming an image locally within a sub-area of an image to adjust image contrast for display by a display device. In yet another aspect, the system may provide a method for transforming an image of a first luminance to adjust a perceived color hue thereof for display by a display device according to a second luminance. In yet another aspect, the system may provide a method for transforming an image having a first luminance to adjust a color saturation thereof for display by a display device having a second luminance.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: A system and method are provided for object tracking in a scene over time. The method comprises obtaining tracking data from a tracking device, the tracking data comprising information associated with at least one point of interest being tracked; obtaining position data from a scene information provider, the scene being associated with a plurality of targets, the position data corresponding to targets in the scene; applying a probabilistic graphical model to the tracking data and the target data to predict a target of interest associated with an entity being tracked; and performing at least one of: using the target of interest to determine a refined point of interest; and outputting at least one of the refined point of interest and the target of interest.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: The following provides a system and method to display images on a display screen (e.g. via a display panel or by projection) and the processing of image data therefor. In one aspect, the system implements a luminance retargeting method for altering the perceived contrast and/or colours of an image to match their appearance under different luminance levels. In another aspect, the system may provide a method for transforming an image locally within a sub-area of an image to adjust image contrast for display by a display device. In yet another aspect, the system may provide a method for transforming an image of a first luminance to adjust a perceived colour hue thereof for display by a display device according to a second luminance. In yet another aspect, the system may provide a method for transforming an image having a first luminance to adjust a colour saturation thereof for display by a display device having a second luminance.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.

Abstract: A method for encoding high dynamic range (HDR) images involves providing a lower dynamic range (LDR) image, generating a prediction function for estimating the values for pixels in the HDR image based on the values of corresponding pixels in the LDR image, and obtaining a residual frame based on differences between the pixel values of the HDR image and estimated pixel values. The LDR image, prediction function and residual frame can all be encoded in data from which either the LDR image of HDR image can be recreated.