Applied Sciences, Free Full-Text
Por um escritor misterioso
Last updated 15 junho 2024
Recent years have witnessed tremendous advances in clouding gaming. To alleviate the bandwidth pressure due to transmissions of high-quality cloud gaming videos, this paper optimized existing video codecs with deep learning networks to reduce the bitrate consumption of cloud gaming videos. Specifically, a camera motion-guided network, i.e., CMGNet, was proposed for the reference frame enhancement, leveraging the camera motion information of cloud gaming videos and the reconstructed frames in the reference frame list. The obtained high-quality reference frame was then added to the reference frame list to improve the compression efficiency. The decoder side performs the same operation to generate the reconstructed frames using the updated reference frame list. In the CMGNet, camera motions were used as guidance to estimate the frame motion and weight masks to achieve more accurate frame alignment and fusion, respectively. As a result, the quality of the reference frame was significantly enhanced and thus being more suitable as a prediction candidate for the target frame. Experimental results demonstrate the effectiveness of the proposed algorithm, which achieves 4.91% BD-rate reduction on average. Moreover, a cloud gaming video dataset with camera motion data was made available to promote research on game video compression.
Applied Sciences An Open Access Journal from MDPI
Applied Sciences An Open Access Journal from MDPI
Applied Sciences An Open Access Journal from MDPI
Salem Press - Applied Science
Applied Sciences, Free Full-Text, Semantic Mediation Model to Promote Improved Data Sharing Using Representa…
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Applied Energy, Journal
Applied Sciences An Open Access Journal from MDPI
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