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Efficient and Fast Light Field Compression via VAE-Based Spatial and Angular Disentanglement
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DOI：10.1109/ACCESS.2025.3532608.png)
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摘要
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Light field (LF) imaging captures both spatial and angular information, which is essential for applications such as depth estimation, view synthesis, and post-capture refocusing. However, the efficient processing of this data, particularly in terms of compression and encoding/decoding time, presents challenges. We propose a Variational Autoencoder (VAE)-based framework to disentangle the spatial and angular features of light field images, focusing on fast and efficient compression. Our method uses two separate sub-encoders-one for spatial and one for angular features-to allow for independent processing in the latent space, which facilitates more streamlined compression workflows. Evaluations on standard light field datasets demonstrate that our approach reduces encoding and decoding time significantly, with a slight trade-off in Rate-Distortion (RD) performance, making it suitable for real-time applications.
Keyword:
Light fields
Image coding
Decoding
Kernel
Image reconstruction
Feature extraction
Training
Imaging
Streaming media
Redundancy
Light field
compression
disentangling
variational auto-encoder
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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