Hierarchical VQ-VAE for Low-Resolution Video Compression
Published:Dec 31, 2025 01:07
•1 min read
•ArXiv
Analysis
This paper addresses the growing need for efficient video compression, particularly for edge devices and content delivery networks. It proposes a novel Multi-Scale Vector Quantized Variational Autoencoder (MS-VQ-VAE) that generates compact, high-fidelity latent representations of low-resolution video. The use of a hierarchical latent structure and perceptual loss is key to achieving good compression while maintaining perceptual quality. The lightweight nature of the model makes it suitable for resource-constrained environments.
Key Takeaways
- •Proposes a novel MS-VQ-VAE for efficient low-resolution video compression.
- •Employs a hierarchical latent structure and perceptual loss for improved quality.
- •Designed for edge devices with limited resources.
- •Achieves competitive PSNR and SSIM scores.
Reference
“The model achieves 25.96 dB PSNR and 0.8375 SSIM on the test set, demonstrating its effectiveness in compressing low-resolution video while maintaining good perceptual quality.”