{'fraction_non_alphanumeric': 0.015734265734265736, 'fraction_numerical': 0.0017482517482517483, 'mean_word_length': 4.932642487046632, '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}}
Efficient bitrate reduction using a Game Attention Model in cloud gaming

Cloud gaming is a new promising service which combines the advantages of cloud computing and online gaming. The concept of cloud gaming is to render a game on a cloud server and stream its scenes to the player as a video sequence over a broadband connection. In order to attract more customers and increase profitability, one needs an efficient and scalable framework to address the heterogeneity and constraints of client devices as well as the network infrastructure. In this paper, we introduce the concept of Game Attention Model (GAM), which is basically a hybrid visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player's perspective and allows encoding less important macro-blocks with lower bit-rate. Subjective assessment shows that by integrating this model into a cloud gaming framework, it is possible to decrease the required bit rate by nearly 50 percent in average, while maintaining a relatively high user quality of experience.