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生成式视频压缩把码率约束引入画面表示学习

当可用带宽很低时，压缩器要决定哪些细节值得花比特保留。研究团队面向约20千比特每秒的视频压缩，将已有视频模型的视觉表示改造成离散编码，并训练一个较小的熵模型，估计这些编码之间的相关性。编码器与熵模型一起训练，使画面转换成符号的方式也能根据预计的编码成本调整。部分符号分组预测，以便并行解码。 Jiancheng Zhao等人的这项研究收录在11月6日更新的计算机科学预印本列表中。

团队在UVG、MCL-JCV和HEVC Class B三组视频上比较了感知质量与码率。相对GLC-Video，基于LPIPS指标的平均码率节省区间为82.2%至87.5%，按DISTS指标衡量则为78.3%至85.0%。这些百分比来自特定参考方案与感知评价曲线，并不是说任意视频文件都可按同一比例缩小。

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