Indicator analysis of integrated models.

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规模、提升编码性能并进一步优化压缩效果，本文针对深度预测分区编码（depth prediction partition encoding）展开研究。在模式选择策略方面，本文基于快速策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，针对赤道与极地区域采用差异化的自适应量化参数调整策略，其目标是实现失真与码流规模间的最优平衡，从而在保障视频画质的同时管控输出码流。实验结果表明，该策略可实现最高2.92%的码率降幅，平均降幅达1.76%；平均编码时长可节省39.28%，平均重建质量提升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分辨率序列中均表现优异。本文提出的深度分区自适应策略在视频编码质量与效率上均有显著提升，可在保障视频画质的同时提高编码效率。