Apparatus and method for automated self-training of white balance by electronic cameras

1. A method for calibrating auto white balancing in an electronic camera, comprising: obtaining, using a processor onboard the electronic camera, a plurality of first color values from a respective first plurality of images of a respective plurality of real-life scenes captured by the electronic camera under a first illuminant, the electronic camera including a plurality of initial auto white balance parameters stored in memory onboard the electronic camera and having only one precalibrated auto white balance parameter, the precalibrated auto white balance parameter being associated with a reference illuminant different from the first illuminant; invoking, using the processor, an assumption about a true color value of at least portions of the real-life scenes, the assumption being stored in the memory; and determining, using a self-training module onboard the electronic camera, a plurality of final auto white balance parameters for a respective plurality of illuminants including the first illuminant and the reference illuminant, the self-training module including the processor and machine-readable self-training instructions stored in the memory that, when executed by the processor, perform the step of determining based upon the true color value, average of the first color values, and the initial auto white balance parameters.

2. The method of claim 1 , the plurality of final auto white balance parameters comprising a final first auto white balance parameter for the first illuminant, and the step of determining comprising: determining, based upon difference between the true color value and the average of the first color values, the final first auto white balance parameter; and transforming the plurality of initial auto white balance parameters, comprising an initial first auto white balance parameter for the first illuminant, to produce the plurality of final auto white balance parameters, the initial first auto white balance parameter being transformed to the final first auto white balance parameter, the step of transforming including using the processor to (a) retrieve the initial auto white balance parameters from the memory, and (b) execute machine-readable transformation instructions stored in the memory to transform the initial auto white balance parameters to the final auto white balance parameters.

3. The method of claim 2 , each of the first images having color defined by a first, second, and third primary color; and the step of transforming being performed in a two-dimensional space spanned by an ordered pair of a first color ratio and a second color ratio, the first and second color ratios together defining the relative values of the first, second, and third primary colors.

4. The method of claim 3 , the step of transforming comprising rotating and scaling the initial white balance parameter set within the two-dimensional space.

5. The method of claim 3 , the ordered pair being [second primary color/third primary color, second primary color/first primary color], [first primary color*third primary color/second primary color^2, third primary color/first primary color], [Log(second primary color/third primary color), Log(second primary color/first primary color)], [Log(first primary color*third primary color/second primary color^2), Log(third primary color/first primary color)], or a derivative thereof.

6. The method of claim 2 , further comprising determining the plurality of initial auto white balance parameters by: obtaining, from images captured by a second electronic camera, a plurality of base auto white balance parameters comprising a base auto white balance parameter for the reference illuminant; calibrating, from images captured by the electronic camera, the base auto white balance parameter to produce the precalibrated auto white balance parameter; and transforming the base auto white balance parameter set to produce the initial auto white balance parameter set, the initial second auto white balance parameter being the precalibrated auto white balance parameter.

7. The method of claim 6 , the step of calibrating comprising capturing, by the electronic camera, a second plurality of images of one or more scenes under the reference illuminant; and the precalibrated auto white balance parameter, when applied to white balance the second plurality of images, yielding an average color of the second plurality of images that is gray.

8. The method of claim 6 , the step of calibrating comprising capturing, by the electronic camera, a second plurality of images of one or more scenes under the reference illuminant, each of the one or more scenes comprising a human face; and the precalibrated auto white balance parameter, when applied to white balance the second plurality of images, yielding an average hue of the human faces that is a universal human facial hue.

9. The method of claim 1 , the step of obtaining comprising selecting the first plurality of images from a superset of images, captured by the electronic camera of real-life scenes, each image in the first plurality of images being captured under the first illuminant, the step of selecting including using the processor to (a) execute machine-readable color value extraction instructions, stored in the memory, to extract color values from the superset of images and, (b) execute machine-readable illuminant identification instructions, stored in the memory, to identify the first plurality of images based upon the color values.

10. The method of claim 1 , each of the first color values being an average color of the respective image; and the true color value being an average color of the plurality of real-life scenes, the assumption being that the average color is gray.

11. The method of claim 1 , each of the first plurality of images comprising at least one human face; each of the first color values defining an average hue of the at least one human face; and the true color value being an average hue of human faces in the plurality of real-life scenes, the assumption being that the average hue is a universal human facial hue.

12. The method of claim 11 , the step of obtaining comprising selecting the first plurality of images from a superset of images, captured by the electronic camera of real-life scenes, each image in the first plurality of images being captured by the first illuminant and including at least one human face, the step of selecting including using the processor to (a) execute machine-readable color value extraction instructions, stored in the memory, to extract color values from the superset of images and, (b) execute machine-readable illuminant identification instructions, stored in the memory, to, based upon the color values, identify a subset of the superset of images captured under the first illuminant, and (c) execute machine-readable face detection instructions, stored in the memory, to identify the first plurality of images as those of the subset of the superset of images that include a human face.

13. The method of claim 1 , in the step of obtaining, the first plurality of images being images captured by the electronic camera when operated by an end-user.

14. An electronic camera device comprising: an image sensor for capturing images of real-life scenes; a processor; and a non-volatile memory including (a) a partly calibrated auto white balance parameter set consisting of a plurality of initial auto white balance parameters with only one precalibrated auto white balance parameter, associated with a reference illuminant, and (b) machine-readable auto white balance self-training instructions that, when executed by the processor, process a subset of the images to produce a fully calibrated auto white balance parameter set specific to the electronic camera, the subset of the images being captured under a first illuminant different from the reference illuminant.

15. The device of claim 14 , the auto white balance self-training instructions comprising an assumption about the real-life scenes.

16. The device of claim 15 , the assumption comprising an assumption that the average color of a plurality of the real-life scenes is gray.

17. The device of claim 15 , the assumption comprising an assumption that the hue of human faces is a universal human facial hue.

18. The device of claim 14 , the auto white balance self-training instructions comprising: illumination identification instructions that, when executed by the processor, identify the subset of the images captured under the first illuminant; and auto white balance parameter transformation instructions that, when executed by the processor, transform the partly calibrated auto white balance parameter set to the fully calibrated auto white balance parameter set based on analysis of the images identified using the illumination identification instructions.

19. The device of claim 18 , the auto white balance self-training instructions further comprising face detection instructions that, when executed by the processor, identify human faces in images.