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Related Experiment Videos

QARV++: An Improved Hierarchical VAE for Learned Image Compression.

Yichi Zhang1, Yuning Huang1, Fengqing Zhu1

  • 1Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA.

IEEE Transactions on Circuits and Systems for Video Technology : a Publication of the Circuits and Systems Society
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

QARV++ enhances Hierarchical Variational Autoencoder (HVAE)-based Learned Image Compression (LIC) by addressing key limitations. This improved method achieves superior rate-distortion performance, outperforming VVC Intra mode across multiple datasets.

Keywords:
Hierarchical VAELearned Image Compression

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Hierarchical Variational Autoencoder (HVAE)-based Learned Image Compression (LIC) shows promise but faces limitations.
  • Existing HVAE-LIC models suffer from shared latent mappings, static convolutions, and gradient imbalance during optimization.
  • These issues hinder performance compared to autoregressive models.

Purpose of the Study:

  • To propose QARV++, an enhanced HVAE-based LIC method overcoming current limitations.
  • To improve rate-distortion performance and adaptability in learned image compression.
  • To stabilize variable-rate optimization for more balanced performance across bit rates.

Main Methods:

  • Introduced a disentangled latent mapping mechanism to prevent posterior collapse.
  • Integrated deformable convolutions (DCNNeXt block) for dynamic feature adaptation.
  • Reformulated variable-rate optimization for balanced gradient updates and stable training.

Main Results:

  • QARV++ achieved superior rate-distortion performance compared to existing HVAE-LIC models.
  • Demonstrated significant BD-Rate improvements against VVC Intra mode on standard datasets (-12.20% Kodak, -16.34% Tecnick, -15.23% CLIC2020).
  • Showcased effective generalization to existing LIC methods, yielding substantial enhancements.

Conclusions:

  • QARV++ effectively addresses limitations in HVAE-based LIC, offering improved performance and adaptability.
  • The proposed methods contribute to more stable and efficient learned image compression.
  • QARV++ represents a significant advancement in HVAE-based image compression technology.