A Self-Supervised Learning Framework for Video Encoding Complexity Clustering
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 o...
| Authors | Krishna Srikar Durbha, Hassene Tmar, Ping-Hao Wu et al. |
|---|---|
| Primary category | eess.IV |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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