论文标题
部分可观测时空混沌系统的无模型预测
Codec Compression Efficiency Evaluation of MPEG-5 part 2 (LCEVC) using Objective and Subjective Quality Assessment
论文作者
论文摘要
随着HEVC,HEVC,AV1和VVC等新的编解码器提高的视频压缩效率的进步,以及智能编码策略以及提高的带宽可用性的智能编码效率,因此,诸如Netflix,Twitch等新型服务的繁殖和接受,例如,Twitch等较高的延迟延迟和延迟的延迟,并且具有更高的延迟性。因此,需要更快,更有效的编解码器以实现更高的编码效率,而在复杂性和速度方面都没有重大折衷。我们在这项工作中介绍了最新的MPEG-5第2部分低复杂性增强视频编码(LCEVC),用于实时游戏视频流应用程序。使用主观和客观质量措施以及对编码速度的比较,以比特率节省来提出结果。我们的结果表明,对于这项工作中使用的编码设置,LCEVC使用VMAF的比特率节省量优于X264和X265编解码器,大约为42 \%和38 \%。使用主观结果,发现LCEVC的表现优于各个基本编解码器,尤其是对于低比特率。对于X264而言,这种效果比X265更明显,即,对于后者,质量得分的绝对提高较小。作为开放数据集的一部分,在https://github.com/nabajeetbarman/lcevc-livegaming上提供了客观和主观结果以及示例视频序列。
With the increasing advancements in video compression efficiency achieved by newer codecs such as HEVC, AV1, and VVC, and intelligent encoding strategies, as well as improved bandwidth availability,there has been a proliferation and acceptance of newer services such as Netflix, Twitch, etc. However, such higher compression efficiencies are achieved at the cost of higher complexity and encoding delay, while many applications are delay sensitive. Hence, there is a requirement for faster, more efficient codecs to achieve higher encoding efficiency without significant trade-off in terms of both complexity and speed. We present in this work an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for live gaming video streaming applications. The results are presented in terms of bitrate savings using both subjective and objective quality measures as well as a comparison of the encoding speeds. Our results indicate that, for the encoding settings used in this work, LCEVC outperforms both x264 and x265 codecs in terms of bitrate savings using VMAF by approximately 42\% and 38\%. Using subjective results, it is found that LCEVC outperforms the respective base codecs, especially for low bitrates. This effect is more evident for x264 than for x265, i.e., for the latter the absolute improvement of quality scores is smaller. The objective and subjective results as well as sample video sequences are made available as part of an open dataset, LCEVC-LiveGaming at https://github.com/NabajeetBarman/LCEVC-LiveGaming.