Video compression systems must support increasing bandwidth and data throughput at low cost and power, and can be limited by entropy coding bottlenecks. Efficiency can be greatly improved by parallelizing coding, which can be done at much larger scales with new neural-based codecs, but with some compression loss related to data organization. We analyze the bit rate overhead needed to support multiple bitstreams for concurrent decoding, and for its minimization propose a method for compressing parallel-decoding entry points, using bidirectional bitstream packing, and a new form of jointly optimizing arithmetic coding termination. It is shown that those techniques significantly lower the overhead, making it easier to reduce it to a small fraction of the average bitstream size, like, for example, less than 1% and 0.1% when the average number of bitstream bytes is respectively larger than 95 and 1,200 bytes.

翻译：视频压缩系统必须在不增加成本与功耗的前提下支持日益增长的带宽与数据吞吐量，而熵编码瓶颈可能限制其性能。通过并行化编码可大幅提升效率，结合新型神经编解码器可在更大规模上实现该目标，但数据组织方式会引入一定压缩损失。本文分析了支持多比特流并发解码所需的比特率开销，并针对其最小化问题提出了一种方法：采用双向比特流打包压缩并行解码入口点，并引入一种联合优化算术编码终止的新形式。实验表明，这些技术能显著降低开销，使其易于缩减至平均比特流大小的极小比例——例如，当比特流平均字节数分别大于95和1,200字节时，开销可分别低于1%和0.1%。