ArticleIEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society2026
QARV++: An Improved Hierarchical VAE for Learned Image Compression.
Article in IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Hierarchical Variational Autoencoder (HVAE)-based Learned Image Compression (LIC) has shown great promise, but its performance still lags behind autoregressive models due to three key limitations identified: (1) shared latent mappings that lead to accumulated posterior collapse; (2) reliance on static convolutions, limiting adaptability; and (3) gradient imbalance during variable-rate optimization, causing unbalanced performance across different bit rates. To overcome these challenges, we propose QARV++, an improved HVAE-based LIC method. First, we introduce a disentangled latent mapping mechanism, assigning separate transformations to each latent variable to prevent posterior collapse propagation. Second, we integrate deformable convolutions into the network, introducing the DCNNeXt block, which enables dynamic feature adaptation while maintaining computational efficiency. Third, we reformulate variable-rate optimization to ensure balanced gradient updates across different
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.