{'fraction_non_alphanumeric': 0.016101694915254237, 'fraction_numerical': 0.006779661016949152, 'mean_word_length': 4.78921568627451, '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 new post-processing method for low bit rate JPEG coded images

At low bit rates (0.2bpp or below), images coded by JPEG suffer severe visual distortions. These distortions include blurred edges due to truncation of high frequency coefficients and blocking artifacts due to the block-based nature of JPEG. In this paper, we propose techniques to reduce these two artifacts so as to obtain an image of better visual quality as well as higher PSNR. For blocking artifacts, we adjust the low frequency DCT coefficients by minimizing abrupt changes in the image under certain constraint. The abrupt changes are measured using the mean square values of the difference between adjacent pixels. For blurred edges, we first detect them using the Canny edge detector and then represent them using an edge model with their parameters estimated. The distortion around these edges can then be removed by adjusting the parameters of the edge model under the quantization constraint. The method requires about four seconds for a 512x512 image using a high-end PC. Experimental results show that the proposed approach can improve the visual quality as well as the fidelity of JPEG coded images.