4.5 Article

Latitude-Based Flexible Complexity Allocation for 360-Degree Video Coding

期刊

IEEE TRANSACTIONS ON BROADCASTING
卷 68, 期 3, 页码 572-581

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBC.2022.3174477

关键词

Complexity theory; Encoding; Video coding; Resource management; Multimedia communication; Broadcasting; Optimization methods; 360-degree video; panoramic video; video coding; complexity optimization

资金

  1. National Natural Science Foundation of China [62171134]
  2. Natural Science Foundation of Fujian Province [2019J01222]

向作者/读者索取更多资源

This paper proposes a flexible complexity optimization method for 360-degree video coding, which allows the encoder to adapt to different complexity constraints in various broadcasting scenarios. The method achieves a reduction in computational time while maintaining high visual quality, as shown through comprehensive experiments.
The emerging 360-degree video has a good practical prospect in video broadcasting due to its more immersive experience than traditional videos. To maintain a high fidelity with panoramic vision, 360-degree video adopts a significantly increased resolution than planar videos, which also inevitably leads to higher encoding complexity. In this paper, we propose a flexible complexity optimization method for 360-degree video coding, in which the encoder is capable of adapting to different complexity constraints in diversified broadcasting scenarios. In particular, the proposed method is realized with a latitude-based computation complexity allocation. Firstly, it investigates the Coding Unit (CU) partition variation along the latitude (i.e., vertical axis) and divides each video frame into Largest CU (LCU)-based latitude regions. Then, it formulates the complexity allocation problem from the global complexity target to all latitude regions, by considering the intraframe dependencies and Rate-Distortion (RD) models. Finally, it calculates the solution to this complexity allocation problem and further employs it in 360-degree video coding. Comprehensive experiments on the standard dataset reveal the superiority of our method, which achieves a flexible computational time reduction from 14.53% to 68.36% whilst maintaining a high visual quality of compressed videos.

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