The dissertation proposes the use of a multi-objective optimization framework for designing and selecting among enhanced GOP configurations in video compression standards. The proposed methods achieve fine optimization over a set of general modes that include: (i) maximum video quality, (ii) minimum bitrate, (iii) maximum encoding rate (previously minimum encoding time mode) and (iv) can be shown to improve upon the YouTube/Netflix default encoder mode settings over a set of opposing constraints to guarantee satisfactory performance. The dissertation describes the implementation of a codec-agnostic approach using different video coding standards (x265, VP9, AV1) on a wide range of videos derived from different video datasets. The results demonstrate that the optimal encoding parameters obtained from the Pareto front space can provide significant bandwidth savings without sacrificing video quality. This is achieved by the use of effective regression models that allow for the selection of video encoding settings that are jointly optimal in the encoding time, bitrate, and video quality space. The dissertation applies the proposed methods to x265, VP9, AV1 and using new GOP configurations in x265, delivering over 40% of the optimal encodings in two standard reference videos.

中文翻译：

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针对HEVC，VP9，AV1和VVC压缩标准中的受限视频传输以及适应视频内容的自适应编码

本文提出了一种多目标优化框架，用于设计和选择视频压缩标准中的增强型GOP配置。所提出的方法在一组通用模式上实现了精细的优化，这些通用模式包括：（i）最大视频质量，（ii）最小比特率，（iii）最大编码率（以前是最小编码时间模式），并且（iv）可以改善在YouTube / Netflix的默认编码器模式设置上设置了一系列相反的限制条件，以确保令人满意的性能。本文介绍了在不同视频数据集衍生的各种视频上使用不同的视频编码标准（x265，VP9，AV1）实现与编解码器无关的方法的实现。结果表明，从帕累托前端空间获得的最佳编码参数可以在不牺牲视频质量的情况下节省大量带宽。这是通过使用有效的回归模型实现的，该模型允许选择在编码时间，比特率和视频质量空间上共同最佳的视频编码设置。本文将提出的方法应用于x265，VP9，AV1，并在x265中使用新的GOP配置，在两个标准参考视频中提供了40％以上的最佳编码。