{'fraction_non_alphanumeric': 0.011350737797956867, 'fraction_numerical': 0.0, 'mean_word_length': 5.211267605633803, '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}}
Nonredundant image representations

We consider the problem of sending raw image data over lossy or heterogeneous network connections with as little overhead as possible. If the network connection does not support multiple priority levels and the network drops packets at random, a technique is needed where the data would be divided into parts of equal importance, so that the image could be at least partially reconstructed from the packets available. We found theoretical criteria for an image to be broken into pieces of equal importance, developed an algorithm for such criteria to be implemented and devised a signal processing scheme based on critically-sampled fast implementation filter banks. Experiments show that although the overhead is not large, it can further be reduced by using a scrambler. This scheme also solves the problem of holographic image representation.