Entropy-Coded MS-VQ-VAE with Learned Priors for Ultra-Low Bitrate Video Compression
This paper presents an entropy-coded MS-VQ-VAE framework that leverages discrete latent representations and learned autoregressive priors to achieve ultra-low bitrate video compression. Operating at 0.043-0.064 bpp on UCF101, the method outperforms H.265 in perceptual quality while using 5-7.6× fewer bits.
-->
[Submitted on 28 Jun 2026]
Title:Entropy-Coded MS-VQ-VAE with Learned Priors for Ultra-Low Bitrate Video Compression
View a PDF of the paper titled Entropy-Coded MS-VQ-VAE with Learned Priors for Ultra-Low Bitrate Video Compression, by Manikanta Kotthapalli and 1 other authors
View PDF HTML (experimental)
Abstract:Learned video codecs based on continuous latent representations struggle to operate reliably below 0.1 bits per pixel~(bpp): without a differentiable rate signal, Lagrangian optimisation cannot effectively trade reconstruction quality for bitrate at extreme compression ratios. We demonstrate that discrete latent representations sidestep this limitation entirely. In a vector-quantized~(VQ) codec, the codebook size~$K$ imposes a hard information ceiling of $\log_2 K$ bits per symbol; a learned autoregressive prior then exploits the non-uniform distribution of code usage -- which we show follows a power law -- to push actual bitrates well below this ceiling, without any rate-penalty tuning.
Building on the MS-VQ-VAE architecture introduced in~\cite{kotthapalli2026msvqvae}, we sweep $K \in \{128, 256, 512, 1024\}$ under a uniform training protocol to trace four operating points on the rate-distortion~(RD) curve. We identify and resolve a critical training instability: gradient-based VQ collapses catastrophically at $K \leq 512$, whereas EMA-stabilised codebook updates with dead-code restart maintain full utilisation across all configurations. On 500 UCF101 test clips ($64\!\times\!64$, 32~frames), our models operate at 0.043-0.064~bpp -- 3.3-5$\times$ below H.264's practical floor and $5$-$7.6\times$ below H.265's floor at this resolution. Every MS-VQ-VAE configuration outperforms H.265 CRF\,36 on perceptual quality (LPIPS) despite using $5$-$7.6\times$ fewer bits. At $K{=}1024$, the model surpasses H.265 CRF\,36 on LPIPS by a margin of 0.072 absolute while using $5.1\times$ fewer bits. Codebook analysis confirms power-law index distributions and 70-85\% entropy efficiency, establishing the pipeline as a principled learned entropy coder.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.02562 [cs.CV]
(or arXiv:2607.02562v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.02562
arXiv-issued DOI via DataCite
Submission history
From: Manikanta Kotthapalli [view email] [v1] Sun, 28 Jun 2026 20:09:45 UTC (758 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Entropy-Coded MS-VQ-VAE with Learned Priors for Ultra-Low Bitrate Video Compression, by Manikanta Kotthapalli and 1 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-07
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)