Streaming media is important in modern information consumption industry. However the limited and varied computing resources and bandwidth of client devices pose challenges for video coding. To make a balance between speed and coding efficiency and provide scalability for different devices, we propose a hybrid scalable coding framework (HSVC) which maintains a layered structure including a low resolution basement layer encoded by traditional coding methods and an optional enhancement layer encoded by neural coding methods. The enhancement layer coding is designed with a neural contextual compression module and a neural motion vector-guided enhancement module, which make full use of inter-layer information to boost the performance of enhancement layer and produce better visual quality. Experiments demonstrate that the proposed method outperforms existing scalable coding methods.

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Hybrid Scalable Video Coding with Neural Compression and Enhancement for Streaming Media

  • Yuyao Ye,
  • Jiayu Yang,
  • Yang Zhao,
  • Mengping Gao,
  • Hongbin Cao,
  • Ronggang Wang

摘要

Streaming media is important in modern information consumption industry. However the limited and varied computing resources and bandwidth of client devices pose challenges for video coding. To make a balance between speed and coding efficiency and provide scalability for different devices, we propose a hybrid scalable coding framework (HSVC) which maintains a layered structure including a low resolution basement layer encoded by traditional coding methods and an optional enhancement layer encoded by neural coding methods. The enhancement layer coding is designed with a neural contextual compression module and a neural motion vector-guided enhancement module, which make full use of inter-layer information to boost the performance of enhancement layer and produce better visual quality. Experiments demonstrate that the proposed method outperforms existing scalable coding methods.