{'fraction_non_alphanumeric': 0.011627906976744186, 'fraction_numerical': 0.0, 'mean_word_length': 5.271523178807947, '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}}
A Weighted Least Squares Method For Adaptive Prediction in Lossless Image Compression

This paper is concerned with the application of the least squares method in predictive lossless image compression. Based on the observation of the limitations of ordinary least squares (OLS) method that is employed in most published algorithms, a unique weighted least squares (WLS) method is proposed. This WLS method gives a weight to the contribution of an observed sample according to the similarities between the feature at this sample and that at the pixel to be predicted. Experimental results have confirmed the superiority of the proposed method over both the OLS based method and WLS methods using alternative weighting schemes. It has been demonstrated that when coupled with other components of a lossless image compression algorithm, the proposed method can achieve a compression performance better than any other algorithms known to the authors.