Related papers: A Self-Supervised Learning Framework for Video Encoding Complexity Clustering

A Self-Supervised Learning Framework for Video Encoding Complexity Clustering

URL: http://arxiv.org/abs/2606.29166v1
Date: Sun, 28 Jun 2026 03:01:10 GMT
Title: A Self-Supervised Learning Framework for Video Encoding Complexity Clustering
Abstract summary: Adaptive video streaming is a widely used technique for delivering video content over the internet.<n>One of the key challenges is determining the optimal encoding settings for each video, which can vary significantly based on its content and characteristics.<n>We propose Compression Echo Contrastive Learning, a novel self-supervised learning framework for clustering videos based on their encoding complexity.
Score: 25.158228645127036
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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