Television – Camera – system and detail – Combined image signal generator and general image signal...
Reexamination Certificate
2000-12-11
2004-08-03
Ho, Tuan (Department: 2612)
Television
Camera, system and detail
Combined image signal generator and general image signal...
C348S647000
Reexamination Certificate
active
06771311
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to methods of processing digital color images, and more particularly to a method of adjusting the color saturation of a digital color image.
BACKGROUND OF THE INVENTION
In photographic printing of film, such as a color photographic negative, it is a well-known practice to ‘correct’ the color balance by causing the overall color balance of the print to be a shade near gray. This correction strategy is based on the assumption that the overall average of the scene integrates to a gray color and is very effective at reducing the effects resulting from different scene illuminants that are spectrally different such as tungsten and daylight. In a like manner, image sensing apparatus such as a video camera, average, over a time period, color difference signals, R-Y and B-Y, to a zero value. This is equivalent to integrating to gray.
These methods work well for the majority of scene and illuminant combinations. However, when the scene subject matter is highly colored, particularly with a single dominant color, the integrate to gray strategy fails as this dominant scene color is mistaken for an illuminant bias. This failure, known as subject failure, produces unpleasant colored casts in the color complimentary to the dominant scene color. There are various strategies for minimizing these failures. These strategies are typically based on reducing the amount of correction based on a population of images and/or on the information in neighboring frames. Other color problems result from fading of the dyes in a photographic image, printing and processing errors, film keeping problems, and in the case of a color digital image that is captured directly with an electronic camera, a misadjusted black point in the camera.
With a digital image, obtained either directly from an electronic camera, or indirectly by scanning a photographic print or film, it is possible to manually adjust the color balance by using any of the well known digital photographic manipulation tools such as Adobe Photoshop®. However, manual adjustment is not practical for automated printing of digital images. A digital image provides much information that can be used for calculating color adjustments, and several methods have been proposed for performing these adjustments automatically. Some of these methods, such as that taught in U.S. Pat. No. 5,555,022, issued Sep. 10, 1996 to Haruki et al. divide the scene information into a plurality of regions representing different locations within a scene. Means to select and weight the correction of these blocks are then employed to provide automatic white balancing and to restrict the degree to which color correction gain is applied. Yonezawa, U.S. Pat. No. 4,984,071 teaches a method for adjusting a gradation curve based on identifying shadow and highlight points by utilizing histogram data. Morikawa, U.S. Pat. No. 5,062,058 describes a system that uses a cumulative histogram to designate highlight and shadow points. Other histogram-based methods are taught by Lin, U.S. Pat. No. 5,812,286 and Edgar, U.S. Pat. No. 5,265,200. Edgar further describes a method performing a second order best fit regression of the histogram data and includes methods to eliminate some histogram values from consideration in the regression.
Another approach that combines color correction with tone scale corrections is based on random sampling within a digitized image and subsequently modifying the resulting histogram of these samples. U.S. Pat. No. 4,677,465, issued Jun. 30, 1987 to Alkofer and U.S. Pat. No. 4,729,016, issued Mar. 1, 1988 to Alkofer disclose relatively complex methods that utilize these samples in a plurality of segmented contrast intervals through normalization techniques and with comparison to image population data.
An improved method for simultaneously adjusting color scale and color balance was disclosed in U.S. patent application Ser. No. 09/650,422, filed Aug. 29, 2000, that preferably calculates gain and offset factors by regressing masked red, green, and blue digital image data to a masked reference digital image.
While the above described methods can correct for tone scale, they do not provide method or means for enhancing color degradation resulting from the loss of color purity or color saturation. This type of color degradation can be restored if channel interdependent image processing, such as a 3×3 color matrix, is applied.
U.S. Pat. No. 5,796,874 by Woolfe et al. describes an approach that is specifically designed to correct for dye-fade in Ektacolor paper includes generating a 3×3 restoration matrix that can compensate for this loss of purity or color saturation. The disclosed restoration matrix is a matrix of coefficients of second order polynomials in time, with factors that are optimized for specific paper, are applied in logarithmic space, and which compensate for the image-wise light filtration from overlying layers in the film. The compensation matrix is followed by tone-scale adjustments. This method assumes that additional information, time and type of photographic material, are known. This information is oftentimes not available. In addition, parameters other than time, such as temperature and light exposure, can impact stability and long term purity of dye images.
Another method to generate a color restoration matrix is taught by Edgar, U.S. Pat. No. 5,673,336, wherein determining the correlation of the noise in the digital image is used to generate and apply a decorrelating matrix to restore the image. This method has the advantage that it does not require any additional information on the pedigree of the image; however, it assumes that there is a detectable noise signal, that this noise signal is not contaminated with image signal, and that in the original, non-degraded image, the image noise was fully decorrelated and was of near equal power levels amongst the channels. It is difficult, or in many cases impossible, to guarantee that these assumptions are met.
It has been further observed that when digital images of dye faded images have been processed through many of the above methods that provide both gain and offset tone scale corrections to each channel that a uniform color boost among the color channels, color boost, in which a color restoration matrix with all diagonal terms equal and with all off-diagonal terms equal, is effective at restoring color saturation. One embodiment of the method taught by Katsuma in U.S. Pat. No. 5,398,123 uses such a uniform boost to increase color saturation. Katsuma teaches a method and apparatus to increase color saturation wherein at each pixel, the difference between the maximum value amongst a plurality of color components at that pixel and the minimum value amongst a plurality of color components at each pixel is calculated. These calculated differences for each pixel are averaged for the entire image, and if this average exceeds a threshold, a correction factor, based on the dynamic range of the image and this average, is applied to the image as a uniform saturation boost. This saturation boost is applied as a global change, that is, every pixel is subjected to the same image processing. He notes that this operation can result in overflow problems and solves these problems by mapping overflow in any of the color components at any pixel to the maximum or minimum value supported by the image processing means, e.g. 255 and 0 respectively for an 8 bit per color component imaging system. Furthermore, his method requires that an operator select the above-mentioned dynamic range at the initial step in the enhancement process.
In a similar approach to enhance color saturation, Shu in U.S. Pat. No. 5,517,335 teaches a method wherein at each pixel, the maximum value and the minimum value amongst a plurality of color components are calculated. His method differs from that disclosed by Katsuma in that Shu calculates a modification to each pixel based on only the color component values at that pixel. The difference between the maximum and minimum value at a pixel and th
Eastman Kodak Company
Ho Tuan
Noval William F.
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