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A lightweight perceptual-guided VQVAE for high-fidelity image compression.
Zhisong Bie1, Yunyang Kuang2, Haobo Lei2
1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. zhisongbie@bupt.edu.cn.
Scientific Reports
|May 19, 2026
Summary
HiRes-VQ, a lightweight perceptual-guided VQ-VAE, resolves the image compression quality-efficiency dilemma. It achieves high-fidelity reconstructions with minimal parameters, outperforming complex models.
Area of Science:
- Computer Vision
- Machine Learning
- Image Processing
Background:
- Low-bit-rate image compression struggles with a quality-efficiency trade-off.
- Lightweight models like VQ-VAE yield degraded reconstructions.
- High-quality models (VQGAN, diffusion) are computationally expensive.
Purpose of the Study:
- Introduce HiRes-VQ, a lightweight perceptual-guided VQ-VAE.
- Achieve high-fidelity image reconstruction without compromising efficiency.
- Bridge the gap between lightweight and high-quality compression models.
Main Methods:
- Utilize VQ-VAE-2's hierarchical quantization.
- Implement an asymmetric encoder-decoder architecture.
- Employ a multi-scale perceptual alignment loss function.
Main Results:
- HiRes-VQ achieves 18%-40% fidelity gains over similar-sized baselines.
- Surpasses high-complexity models in quality-efficiency trade-off.
- Demonstrates significant improvements in pixel-level fidelity and semantic perceptual quality.
Conclusions:
- HiRes-VQ effectively resolves the quality-efficiency dilemma in image compression.
- Offers a practical solution for resource-constrained deployment.
- Dual-path decoder and perceptual loss are key innovations.
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