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Content adaptive spatial-temporal rescaling for video coding optimization
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AI
DOI：10.1016/j.eswa.2024.124482.png)
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Abstract
En 中文
With the increase of resolution and frame rate, videos can provide users with a better viewing experience. However, the amount of data is also greatly increased, making it much more difficult for storage and transmission. To address this challenge, we propose to optimize video coding configurations based on video content, aiming to reduce bitrate while preserving perceptual quality. In our method, we extract handcrafted spatial-temporal features from original videos to delineate their content characteristics. Subsequently, we employ support vector machines (SVM) to perform video coding optimization through spatial-temporal rescaling, thereby improving video coding efficiency. Our approach is compatible with existing encoders and decoders, which is advantageous in practical applications. Experimental results on videos with different contents demonstrate that our method can predict the optimal encoding configuration with high accuracy, achieving considerable bitrate savings for videos of various resolutions and frame rates with low computational costs.
Keywords:
Video coding optimization
Rate-distortion optimization
Adaptive resolution
Adaptive frame rate
Journal
IF:
7.5
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2.9W
Citations:
10.2W