{'fraction_non_alphanumeric': 0.01671583087512291, 'fraction_numerical': 0.0009832841691248771, 'mean_word_length': 5.204268292682927, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Color correction for projected image on colored-screen based on a camera

Recently, projector is one of the most common display devices not only for presentation at offices and classes, but for entertainment at home and theater. The use of mobile projector expands applications to meeting at fields and presentation on any spots. Accordingly, the projection is not always guaranteed on white screen, causing some color distortion. Several algorithms have been suggested to correct the projected color on the light colored screen. These have limitation on the use of measurement equipment which can't bring always, also lack of accuracy due to transform matrix obtained by using small number of patches. In this paper, color correction method using general still camera as convenient measurement equipment is proposed to match the colors between on white and colored screens. A patch containing 9 ramps of each channel are firstly projected on white and light colored screens, then captured by the camera, respectively, Next, digital values are obtained by the captured image for each ramp patch on both screens, resulting in different values to the same patch. After that, we check which ramp patch on colored screen has the same digital value on white screen, repeating this procedure for all ramp patches. The difference between corresponding ramp patches reveals the quantity of color shift. Then, color correction matrix is obtained by regression method using matched values. Differently from previous methods, the use of general still camera allows to measure regardless of places. In addition, two captured images on white and colored screen with ramp patches inform the color shift for 9 steps of each channel, enabling accurate construction of transform matrix. Nonlinearity of camera characteristics is also considered by using regression method to construct transform matrix. In the experimental results, the proposed method gives better color correction on the objective and subjective evaluation than the previous methods.