Minimal data set definition.

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality.

为降低编码复杂度与码流尺寸、优化编码性能并进一步提升压缩效能，本文针对深度预测分区编码（depth prediction partition encoding）展开研究。在模式选择策略层面，本文基于快速战略决策方法开展优化分析，以保障数据处理的全面性。在自适应策略设计环节，考虑到360度虚拟现实视频中用户关注度的区域差异，针对赤道区域与极地区域采用不同的自适应量化参数调整策略（adaptive quantization parameter adjustment strategies），其目标是在失真度与码流尺寸之间实现最优平衡，从而在保障视频画质的同时管控输出码流规模。实验结果表明，该策略可实现最高2.92%的比特率（bit rate）降幅与平均1.76%的比特率缩减；平均编码时长可节省39.28%，平均重建质量可达0.043，且观众几乎无法感知画质损失。同时，该模型在4K、6K及8K分辨率视频序列中均表现优异。本文提出的深度分区自适应策略（deep partitioning adaptive strategy）在视频编码质量与编码效率上均有显著提升，可在保障视频画质的同时优化编码效率。