用自适应质量参数压缩360度视频，提升效率且几乎不增加耗时。
Neural Compression of 360-Degree Equirectangular Videos using Quality Parameter Adaptation
- 基于纬度调整压缩质量，利用球面采样密度差异优化率失真。
- 在JVET S1数据集上实现5.2%的码率节省，处理时间仅增0.3%。
- 无需重新训练，适配任意质量参数，解决量化误差问题。
本研究提出一种实用方法，通过预训练的神经视频压缩（NVC）模型压缩360度等距投影视频。无需额外训练或修改模型结构，该方法将传统视频编码器中的量化参数自适应技术拓展至NVC，利用等距投影中空间采样密度的差异性。通过率失真优化引入基于纬度的自适应质量参数，并采用向量库插值进行潜在特征调制，支持灵活的任意质量参数适配，缓解了自适应量化参数中舍入误差带来的限制。实验结果表明，将该方法应用于DCVC-RT框架，在JVET类S1测试序列上以加权球面峰值信噪比衡量，获得5.2%的BD-Rate节省，处理时间仅增加0.3%。
原文摘要 · Abstract (English)
This study proposes a practical approach for compressing 360-degree equirectangular videos using pretrained neural video compression (NVC) models. Without requiring additional training or changes in the model architectures, the proposed method extends quantization parameter adaptation techniques from traditional video codecs to NVC, utilizing the spatially varying sampling density in equirectangular projections. We introduce latitude-based adaptive quality parameters through rate-distortion optimization for NVC. The proposed method utilizes vector bank interpolation for latent modulation, enabling flexible adaptation with arbitrary quality parameters and mitigating the limitations caused by rounding errors in the adaptive quantization parameters. Experimental results demonstrate that applying this method to the DCVC-RT framework yields BD-Rate savings of 5.2% in terms of the weighted spherical peak signal-to-noise ratio for JVET class S1 test sequences, with only a 0.3% increase in processing time.
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