Judging a video by its bitstream cover
Yuxing Han, Yunan Ding, Jiangtao Wen, Chen Ye Gan

TL;DR
This paper introduces a fast, bitstream-only video classification method that bypasses decompression, achieving high accuracy and speed on a large YouTube dataset, outperforming traditional techniques significantly.
Contribution
The novel approach classifies videos directly from bitstream data, reducing computational costs and improving performance in low-quality videos, validated on a large-scale dataset.
Findings
Over 80% precision, accuracy, and recall achieved.
Operates 15,000 times faster than real-time for 30fps videos.
Outperforms traditional DTW algorithm by six orders of magnitude.
Abstract
Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially in an age where an immense volume of video content is constantly being generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. Moreover, these methods often suffer from performance degradation in low-quality videos. We present a novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream. We validate our approach using a custom-built data set comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning 11 distinct categories. Our preliminary evaluations indicate precision, accuracy, and recall rates well over 80%. The algorithm…
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Taxonomy
TopicsVideo Analysis and Summarization · Time Series Analysis and Forecasting · Music and Audio Processing
