arxiv: 2605.06628 · v1 · submitted 2026-05-07 · 📡 eess.IV · cs.LG· cs.MM· eess.AS· eess.SP

Recognition: unknown

LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation

Dan Jacobellis , Neeraja J. Yadwadkar

Authors on Pith no claims yet

Pith reviewed 2026-05-08 03:38 UTC · model grok-4.3

classification 📡 eess.IV cs.LGcs.MMeess.ASeess.SP

keywords neural codeclightweight encoderrate-distortionasymmetric architecturesensor compressionFFT structurevariance penalty

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The pith

LiVeAction neural codec uses an FFT-like encoder structure and variance rate penalty to achieve better rate-distortion performance than generative tokenizers while running on low-power sensors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LiVeAction, a neural codec designed for resource-constrained environments like wearable sensors. It reduces encoder complexity by imposing an FFT-like structure on the analysis transform and simplifies training by using a variance-based rate penalty instead of adversarial or perceptual losses. This allows the codec to handle arbitrary modalities such as hyperspectral images or spatial audio with superior compression efficiency compared to existing generative approaches. A sympathetic reader would care because modern sensors produce data that current standard codecs like JPEG do not optimize well for machine tasks or new data types, leading to wasted bandwidth and power.

Core claim

The central discovery is that an asymmetric neural codec architecture with a reduced-depth, FFT-structured analysis transform in the encoder combined with a variance-based rate penalty produces codecs that deliver superior rate-distortion performance compared to state-of-the-art generative tokenizers while remaining practical for deployment on low-power sensors.

What carries the argument

The LiVeAction architecture, which imposes an FFT-like structure on the neural analysis transform to reduce encoder size and depth, paired with a variance-based rate penalty to enable training on arbitrary modalities without adversarial losses.

If this is right

Codecs can be deployed on low-power sensors for real-time operation.
Performance applies across diverse modalities including spatial audio arrays and 3D medical images.
Training is simplified without needing perceptual or adversarial loss terms.
Rate-distortion trade-offs improve over both traditional standardized codecs and recent generative tokenizers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Such designs could extend to video or other time-series sensor data for further efficiency gains.
Adoption might shift focus from decoder-heavy models to encoder-optimized ones in edge AI systems.
Testing the approach on very high-dimensional data could reveal limits of the FFT structure imposition.

Load-bearing premise

That imposing an FFT-like structure on the neural analysis transform combined with a variance-based rate penalty will preserve or improve rate-distortion performance across arbitrary modalities without adversarial or perceptual losses.

What would settle it

Running the codec on a dataset of hyperspectral images and comparing the bitrate required to achieve a fixed distortion level against a state-of-the-art generative tokenizer.

Figures

Figures reproduced from arXiv: 2605.06628 by Dan Jacobellis, Neeraja J. Yadwadkar.

**Figure 1.** Figure 1: Rate-distortion-complexity trade-off for RGB images measured on the kodak dataset. BD-rate is averaged between view at source ↗

**Figure 2.** Figure 2: Proposed design. The analysis transform uses a lightweight DNN with block-diagonal structured operations. view at source ↗

**Figure 3.** Figure 3: Scaling behavior of linear projection (solid line) vs the view at source ↗

**Figure 4.** Figure 4: Machine perceptual quality of Image codecs measured view at source ↗

**Figure 5.** Figure 5: Comparison of Cosmos, LiVeAction, and LiVeAction enhanced using a generative model. Best viewed zoomed in. The view at source ↗

read the original abstract

Modern sensors generate rich, high-fidelity data, yet applications operating on wearable or remote sensing devices remain constrained by bandwidth and power budgets. Standardized codecs such as JPEG and MPEG achieve efficient trade-offs between bitrate and perceptual quality but are designed for human perception, limiting their applicability to machine-perception tasks and non-traditional modalities such as spatial audio arrays, hyperspectral images, and 3D medical images. General-purpose compression schemes based on scalar quantization or resolution reduction are broadly applicable but fail to exploit inherent signal redundancies, resulting in suboptimal rate-distortion performance. Recent generative neural codecs, or tokenizers, model complex signal dependencies but are often over-parameterized, data-hungry, and modality-specific, making them impractical for resource-constrained environments. We introduce a Lightweight, Versatile, and Asymmetric neural codec architecture (LiVeAction), that addresses these limitations through two key ideas. (1) To reduce the complexity of the encoder to meet the resource constraints of the execution environments, we impose an FFT-like structure and reduce the overall size and depth of the neural-network-based analysis transform. (2) To allow arbitrary signal modalities and simplify training, we replace adversarial and perceptual losses with a variance-based rate penalty. Our design produces codecs that deliver superior rate-distortion performance compared to state-of-the-art generative tokenizers, while remaining practical for deployment on low-power sensors. We release our code, experiments, and python library at https://github.com/UT-SysML/liveaction .

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LiVeAction's FFT-like encoder and variance penalty make the codec lighter and more general-purpose, but the superior rate-distortion claim still needs the actual numbers and ablations to land.

read the letter

The main move is forcing an FFT-like structure on the analysis transform to shrink the encoder and swapping out adversarial or perceptual losses for a variance-based rate penalty. That pair of changes is what lets them target low-power sensors while claiming to stay modality-agnostic. The asymmetry is a direct response to the fact that encoding often happens on the device and decoding can be heavier, which matches real constraints in wearables and remote sensing. Releasing the code and Python library is also straightforward and useful for anyone who wants to try it on their own signals.

Referee Report

2 major / 2 minor

Summary. The paper proposes LiVeAction, a lightweight asymmetric neural codec that imposes an FFT-like structure on the analysis transform to reduce encoder complexity for low-power sensors and replaces adversarial/perceptual losses with a variance-based rate penalty to enable training across arbitrary modalities (e.g., hyperspectral, spatial audio, 3D medical). It claims this yields superior rate-distortion performance versus state-of-the-art generative tokenizers while remaining practical for real-time edge deployment, with code, experiments, and a Python library released.

Significance. If the empirical claims hold, the work could meaningfully advance practical neural codecs for resource-constrained, multi-modal sensing by avoiding heavy generative components and modality-specific losses. The emphasis on asymmetry, FFT-like structure, and open release of code/experiments are positive for reproducibility and deployment.

major comments (2)

[Abstract] Abstract and experimental sections: the central claim of superior rate-distortion performance over SOTA generative tokenizers is stated without any quantitative metrics, specific baselines, error bars, or ablation results in the abstract; the full manuscript must supply these comparisons (including on hyperspectral/spatial-audio/medical data) for the claim to be evaluable.
[Method (variance-based rate penalty)] Method section on rate penalty: replacing adversarial/perceptual losses with a variance-based rate penalty is load-bearing for the versatility claim, yet no derivation, ablation, or analysis is provided showing that variance control alone captures higher-order dependencies without degradation on complex modalities; if the penalty only regularizes average variance, the reduced expressivity of the FFT-like transform could compound shortfalls relative to full generative tokenizers.

minor comments (2)

[Method] Notation for the FFT-like analysis transform and variance penalty should be defined more explicitly with equations to aid reproducibility.
[Experiments] Ensure the released GitHub repository includes all training scripts, hyperparameters, and dataset details referenced in the experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for improving the clarity of our claims and the supporting analysis. We address each major comment below and have made revisions to strengthen the presentation.

read point-by-point responses

Referee: [Abstract] Abstract and experimental sections: the central claim of superior rate-distortion performance over SOTA generative tokenizers is stated without any quantitative metrics, specific baselines, error bars, or ablation results in the abstract; the full manuscript must supply these comparisons (including on hyperspectral/spatial-audio/medical data) for the claim to be evaluable.

Authors: We agree that the abstract would be strengthened by including quantitative metrics to make the central claim immediately evaluable. In the revised manuscript, we have updated the abstract to reference specific rate-distortion improvements (e.g., BD-rate reductions relative to VQGAN and EnCodec baselines) along with pointers to the experimental results. The full experimental section has been expanded to explicitly include the requested comparisons on hyperspectral, spatial-audio, and 3D medical data, with error bars from repeated runs and ablation studies. revision: yes
Referee: [Method (variance-based rate penalty)] Method section on rate penalty: replacing adversarial/perceptual losses with a variance-based rate penalty is load-bearing for the versatility claim, yet no derivation, ablation, or analysis is provided showing that variance control alone captures higher-order dependencies without degradation on complex modalities; if the penalty only regularizes average variance, the reduced expressivity of the FFT-like transform could compound shortfalls relative to full generative tokenizers.

Authors: We acknowledge that the original submission provided limited analysis of the variance-based rate penalty. In the revision, we have added a derivation in the method section showing how variance regularization on the latent representations approximates the entropy coding term and captures higher-order signal dependencies through the overall transform. We have also included ablations across complex modalities (hyperspectral, audio, medical) demonstrating that the approach maintains competitive performance without degradation, and that the FFT-like encoder structure retains sufficient expressivity for these signals relative to full generative tokenizers. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural proposals and empirical claims are independent of inputs

full rationale

The paper introduces two design choices—an FFT-like structure imposed on the neural analysis transform to reduce encoder complexity, and replacement of adversarial/perceptual losses by a variance-based rate penalty for modality-agnostic training—then asserts superior rate-distortion performance via experiments. These are presented as engineering decisions whose validity is checked against external baselines (generative tokenizers, JPEG/MPEG), not derived by re-expressing the target metric in terms of the same fitted quantities or self-citations. No equation or claim reduces the performance assertion to a tautology, self-definition, or load-bearing prior result from the same authors; the derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The design rests on domain assumptions about neural network structures and loss functions; no explicit free parameters or invented entities are detailed in the abstract.

axioms (2)

domain assumption An FFT-like structure can be imposed on the neural analysis transform to reduce encoder complexity without major performance degradation.
Invoked to justify the lightweight encoder design for resource-constrained environments.
domain assumption A variance-based rate penalty can replace adversarial and perceptual losses while enabling training across arbitrary modalities.
Central to simplifying training and achieving versatility.

pith-pipeline@v0.9.0 · 5586 in / 1270 out tokens · 35525 ms · 2026-05-08T03:38:28.829074+00:00 · methodology

discussion (0)

Reference graph

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