{'fraction_non_alphanumeric': 0.015904572564612324, 'fraction_numerical': 0.0, 'mean_word_length': 5.631578947368421, '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}}
Decorrelation of multispectral images for lossless compression

Decorrelation is the most important step in lossless compression. There are spatial and spectral redundancies in multispectral images. In this paper, we proposed a new technique that integer wavelet transform is used to remove spatial redundancy and non-linear predictors are used to remove spectral redundancy. For computational reason, the number of spectral predictors is discussed. These techniques result in higher compression ratios.