一种用于视频编码复杂度聚类的自监督学习框架
A Self-Supervised Learning Framework for Video Encoding Complexity Clustering
AI总结：
提出压缩回波对比学习（CECL），一种自监督框架，利用视频对压缩的响应作为监督信号，聚类编码复杂度，提升下游聚类任务性能并节省码率。
摘要：
自适应视频流是一种广泛用于通过互联网传输视频内容的技术。其中一个关键挑战是为每个视频确定最优编码设置，这可能会根据其内容和特征而显著变化。在本文中，我们提出了压缩回波对比学习（CECL），一种新颖的自监督学习框架，用于根据编码复杂度对视频进行聚类。我们的方法利用视频对压缩的响应——压缩回波——作为监督信号，使模型在预训练期间能够捕获底层编码特征。我们进行了大量实验，证明了我们的学习表示在下游任务（按编码复杂度对视频进行聚类）中的有效性。我们的结果表明，CECL改进了现有的最先进视觉编码器，并且与固定码率阶梯相比，实现了显著的码率和质量节省。
英文摘要：
Adaptive video streaming is a widely used technique for delivering video content over the internet. One of the key challenges is determining the optimal encoding settings for each video, which can vary significantly based on its content and characteristics. In this paper, we propose Compression Echo Contrastive Learning (CECL), a novel self-supervised learning framework for clustering videos based on their encoding complexity. Our method leverages the response of a video to compression - the Compression Echo - as a supervisory signal, allowing the model to capture underlying encoding characteristics during pretraining. We conduct extensive experiments to demonstrate the effectiveness of our learned representations for the downstream task of clustering videos by their encoding complexity. Our results show that CECL improves upon existing state-of-the-art visual encoders and delivers strong bitrate and quality savings against the fixed bitrate ladder.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
