{'fraction_non_alphanumeric': 0.024375, 'fraction_numerical': 0.004375, 'mean_word_length': 5.181467181467181, '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}}
Transform coefficients distribution of the future versatile video coding (VVC) standard

The future video coding standard Versatile Video Coding (VVC) presents better encoding performance than the predecessor standards by employing a set of tools, including a multi-type tree block partition structure, more intra prediction directions, multiple transform functions, larger transform with high-frequency zeroing. Therefore, VVC could be more effective to remove the redundancy. Based on our experiments, the transform coefficients distribution (TCD) produced by the encoder of VVC would have a sharper peak. Particularly, the previously widely used Laplacian distribution and Cauchy distribution cannot fit the sharper TCD well. Note that the Laplacian distribution is included in the generalized Gaussian distribution (GGD) with the shape parameter equals one. Moreover, the smaller shape parameter will lead to a sharper peak. With this motivation, we propose to use the GGD with shape parameter equals 1/2, denoted as S/2 distribution, to model the sharper TCD of VVC. The experimental results show that the proposed S/2 distribution outperforms the widely used Laplacian and Cauchy distributions in terms of TCD fitting both in the main body and the tail parts. It also presents competitive performance with GGD, though there is only one parameter in S/2 distribution. We further propose a rate estimation model based on the S/2 distribution. The results show that the model based on the S/2 distribution is more accurate than one based on the Laplacian or Cauchy distribution in rate estimation.