Feature Fusion Enhanced Super Resolution for Low Bitrate Screen Content Compression
Tong Tang, Xin Zhang, Zhidu Li, Jing Yang
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Screen content image/video has been widely applied into cloud game, AR/VR, online education etc., while the transmission and storage of screen content data is limited, and existing compression methods tend to lose details when encoding screen content with low bitrate. To address this challenge, a low bitrate screen content compression method based on super-resolution is proposed in this paper. The method combines super-resolution (SR) and down-sampling techniques to compress screen content while preserving its details. A novel super-resolution network is designed, which enhances detail preservation during feature fusion and eliminates compression distortion artifacts. Compared with the state-of-the-art methods, the proposed method can averagely save bit rate by 19.77% and 26.18% on the SCID and SIQAD datasets respectively under similar quality.