Abstract: The disclosed implementations relate generally to three-dimensional look-up table (3D LUT) techniques for color correcting digital images. In some implementations, a method of correcting images includes: receiving a request to correct a digital image; and in response to the request, correcting at least a portion of the digital image using a 3D LUT.

Abstract: A method, apparatus and programmed computer system provides the ability to process image data as picture screen elements having color components. A three-dimensional space is generated that represents a color spectrum containing the color conponents. A plurality of three-dimensional space transformation matrices are initiated and concatenated to initiate a single three-dimensional space transformation matrix. The plurality of three-dimensional space transformation matrices are populated in reply to user input data. The image data is adjusted by processing each of the color components with the single three-dimensional space transformation matrix.

Abstract: A method of matching image color and or luminance characteristics in an image processing system. In order to match an input image with a reference image, a color transformation M is initialised (601). An output image is copied (602) from the input image. The following sequence of operations is then repeated: Output and reference images are displayed on a system monitor. The user identifies (603) a highlight, shadow or overall region in both images. These regions are processed (604) to identify a difference (605). The difference is concatenated (606) onto transformation M. The output image is updated (607) by processing the input with M.

Abstract: A method of modifying image data in which image colors are to be modified, including a first step of initializing a color vector function, in which color vector is a function of luminance, and then the following repeated steps. A user defines a luminance range (616, 617) and a color vector (620) for that range. The color vector function is updated (601) and a look-up table (407) is generated (602) that is addressable by luminance. Image data (405) is processed by calculating each pixel's luminance and using this to address the red, green and blue values in the look-up table (407). The red, green and blue values so obtained are then added to each pixel's original red, green and blue values, resulting in output image pixels.

Abstract: A method of matching image color and or luminance characteristics in an image processing system. In order to match an input image with a reference image, a color transformation M is initialised (601). An output image is copied (602) from the input image. The following sequence of operations is then repeated: Output and reference images are displayed on a system monitor. The user identifies (603) a highlight, shadow or overall region in both images. These regions are processed (604) to identify a difference (605). The difference is concatenated (606) onto transformation M. The output image is updated (607) by processing the input with M.

Abstract: A method of matching image color and or luminance characteristics in an image processing system. In order to match an input image with a reference image, a color transformation M is initialised (601). An output image is copied (602) from the input image. The following sequence of operations is then repeated: Output and reference images are displayed on a system monitor. The user identifies (603) a highlight, shadow or overall region in both images. These regions are processed (604) to identify a difference (605). The difference is concatenated (606) onto transformation M. The output image is updated (607) by processing the input with M.

Abstract: A method, apparatus, and article of manufacture provide the ability to process three-dimensional image data. A color transformation to be applied to image data is received from the user and concatenated with previous transformations. The concatenated transformations are applied to pixel values for the image data. Thereafter, the values of various parameters are evaluated to obtain and display updated pixel values. A matte may also be extracted and/or defined. For example, a reference color and various parameters may be obtained and used to calculate a transformation that transforms the reference color to an specified point (e.g., an origin) of the 3D space. The transformation may then be applied to each pixel so that each pixel is assigned a matte value according to its transformed values (and selected parameters), and therefore according to its position with respect to the specified reference color.

Abstract: An apparatus for processing image data 607 is provided, which comprises processing means 201, 202, memory means 205, 206, 207, display means 104 and manually operable input means 102, 103, 105, 106, wherein said memory means stores said image data 607 and instructions 602, said image data 607 and said instructions 607 configure said processing means to perform the steps of outputting said image data as picture screen elements 901 to said display means 104; in response to selecting a sample 1404 of said picture screen elements 901 by means of said input means, generating a reference co-ordinate system (1004) within which to process each of said picture screen elements 901, an axis thereof defining a distance; discriminating said picture screen elements (803, 902) and processing (1005) said discriminated picture screen elements according to said distance by means of proportionally averaging (1305) the co-ordinates thereof.

Abstract: A method of matching image color and or luminance characteristics in an image processing system. In order to match an input image with a reference image, a color transformation M is initialised (601). An output image is copied (602) from the input image. The following sequence of operations is then repeated: Output and reference images are displayed on a system monitor. The user identifies (603) a highlight, shadow or overall region in both images. These regions are processed (604) to identify a difference (605). The difference is concatenated (606) onto transformation M. The output image is updated (607) by processing the input with M.

Abstract: A method of modifying image colors in which a user identifies a source color (707) and a destination color (708) having a common luminance (704). Source and destination color volumes (821, 822) are defined by firstly identifying two points (811, 812) opposite in color to the source (707), and secondly by selecting the most distant of white or black (813) as another point. The source color volume (821) and destination color volume (822) have these three points in common. A transformation is then defined (804) to transform from the source to the destination volume. This transformation, when applied (805) to image colors, results in the color change intended by the user.

Abstract: A method of matching image color and or luminance characteristics in an image processing system. In order to match an input image with a reference image, a color transformation M is initialized (601). An output image is copied (602) from the input image. The following sequence of operations is then repeated: Output and reference images are displayed on a system monitor. The user identifies (603) a highlight, shadow or overall region in both images. These regions are processed (604) to identify a difference (605). The difference is concatenated (606) onto transformation M. The output image is updated (607) by processing the input with M.

Abstract: A process of defining a color volume for use in a process of color keying, in which a foreground image (405) is composited against a background image (407). The foreground image (405) contains regions of a background color (723). The user defines diamond shaped areas (611, 621) in PbPr color dimensions, and luminance ranges (612, 622). Tolerance and softness volumes (613, 623) are defined in this way, and a transformation is defined for each. Foreground pixels are processed (602) to determine a background, foreground, or softness condition for a matte (406). Softness represents a partial mix of foreground and background. The mix level is calculated by re-centering (1201) the softness volume, and processing (1206) with an optional sharpness parameter (706).

Abstract: A method, apparatus and programmed computer system for processing image data as picture screen elements having colour components are provided which generate a three-dimensional space representing a colour spectrum containing said colour components, initiate a plurality of three-dimensional space transformation matrices and concatenate said three-dimensional space transformation matrices to initiate a single three-dimensional space transformation matrix. Said plurality of three-dimensional space transformation matrices are populated in reply to user input data and said image data is adjusted by means of processing each of said colour components with said single three-dimensional space transformation matrix.

Abstract: Image data representing a matte, key or control signal is processed. A transitional portion or softness region of the matte in which undesirable transitions occur is identified. The position of the softness region in colour-space is adjusted so as to reduce relative amplitudes of variations within the identified portion.

Abstract: Image data taking the form of a plurality of image pixels is processed. A three dimensional representation of color-space is displayed and the color of pixels within the processed image are analysed such that said analysed pixels are displayed at color related positions within the displayed color-space.

Abstract: Image data is processed to produce control data in which the control data has a region in which varying values define softness. Within said softness region a sub-region is defined in which control values are set to values equal to those outside the softness region.

Abstract: Image data having a plurality of pixel is processed. Each pixel is represented by three color components (RGB) defining a position within color-space. A base color is identified and a distance in color-space between an input color and said base color is calculated. A control value, which may be used as a basis for a chroma-suppress or a chroma-key etc, is produced in response to the calculated distance.

Abstract: Image frames are stored as a plurality of pixel values. An image frame is viewed in which a perceived object (301) is to be distinguished from the remainder of the image frame. Input signals identify control positions in proximity to the boundary of said object. Pixel values are compared around each of the control positions to define a local color transition. Color transitions for the whole object are then produced by a process of interpolation. In this way, a process similar to chroma-keying may be performed without the need to record a foreground object against a specific type of background object. The chroma-keying process is performed locally with respect to a local color transition. This results in local control signals being produced and control signals for the whole object are then produced by a process of interpolation.

Abstract: Image data having a plurality of pixel is processed. Each pixel is represented by three color components (RGB) defining a position within colorspace. A base color is identified and a distance in colorspace between an input color and said base color is calculated. A control value, which may be used as a basis for a chromasuppress or a chromakey etc, is produced in response to the calculated distance.

Abstract: A method of modifying image data in which image colors are to be modified, including a first step of initialising a color vector function, in which color vector is a function of luminance, and then the following repeated steps. A user defines a luminance range (616, 617) and a color vector (620) for that range. The color vector function is updated (601) and a look-up table (407) is generated (602) that is addressable by luminance. Image data (405) is processed by calculating each pixel's luminance and using this to address the red, green and blue values in the look-up table (407). The red, green and blue values so obtained are then added to each pixel's original red, green and blue values, resulting in output image pixels.