A Self-Supervised Learning Framework for Video Encoding Complexity Clustering
For adaptive video streaming, this method enables more efficient encoding by grouping videos with similar complexity, reducing bandwidth usage without quality loss.
CECL, a self-supervised learning framework, clusters videos by encoding complexity using compression response as a supervisory signal, achieving improved bitrate and quality savings over fixed bitrate ladders.
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.