{'fraction_non_alphanumeric': 0.016927083333333332, 'fraction_numerical': 0.0, 'mean_word_length': 5.629310344827586, '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}}
Device-independent color correction for multimedia applications using neural networks and abductive modeling approaches

Although color has appeal for developers and consumers alike, color reproduction poses a major problem in many computer based applications inducting multimedia and desktop publishing. The problem arises because of device independence of color, and the way each device processes color. In order to control the error in porting color, different traditional techniques have been applied. In this paper the utilization of artificial neural networks as well as abductive modelling approaches to color error reduction are introduced from an RGB (red/green/blue) color model perspective. Analysis of the results and ongoing research issues are discussed.