EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database

• URL: http://arxiv.org/abs/2607.18187v1
• Date: Mon, 20 Jul 2026 17:26:21 GMT
• Title: EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database
• Abstract summary: We present EVOLVE, an autoencoder (AE)-based volume-compression framework that targets high compression ratios for offline compression.<n>First, we construct a large-scale cross-domain database of 6,376 volumes from 21 scientific simulations, curated via perceptual hashing to ensure diversity.<n>Second, we reexamine the design space of AE-based compressors and incorporate several macro- and micro-designs into a vanilla AE to develop EVOLVE, which substantially improves the expressive power and compression capability.<n>Third, we develop a learnable gain mechanism with a three-stage training strategy to enable variable-rate encoding
• Score: 6.618761947019336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical. However, conventional compressors often struggle to preserve fine structural details at high compression ratios (CRs), and implicit neural representations (INRs) require costly per-volume optimization and produce models with fixed CRs. To respond, we present EVOLVE, an autoencoder (AE)-based volume-compression framework that targets high CRs for offline compression, with three key contributions. First, we construct a large-scale cross-domain database of 6,376 volumes from 21 scientific simulations, curated via perceptual hashing to ensure diversity, enabling the optimized model to extract features that generalize across volumes within the covered scientific simulation domains. Second, we reexamine the design space of AE-based compressors and incorporate several macro- and micro-designs into a vanilla AE to develop EVOLVE, which substantially improves the expressive power and compression capability. Third, we develop a learnable gain mechanism with a three-stage training strategy to enable variable-rate encoding, allowing a single model to support continuous CR adjustment at inference time. Experiments on multiple unseen scientific simulation datasets demonstrate that EVOLVE achieves substantially higher CRs than conventional compressors at comparable reconstruction quality, while delivering compression speeds that are orders of magnitude faster than INR-based methods, highlighting its promise as a strong alternative for compressing scientific data. The code, model weights, and results are available on our project page at https://evolve-vis.github.io.

Related papers

• Qwen-Image-VAE-2.0 Technical Report [62.24146940157627]
  We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs)<n>Qwen-Image-VAE-2.0 achieves state-of-the-art reconstruction performance, demonstrating exceptional capabilities in both general domains and text-rich scenarios at high compression ratio.
  arXiv  Detail & Related papers  (2026-05-13T14:04:56Z)
• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting [57.73006852239138]
  We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS)<n>Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications.<n>Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes.
  arXiv  Detail & Related papers  (2026-01-19T08:21:45Z)
• Foundation Model for Lossy Compression of Spatiotemporal Scientific Data [11.494915987840876]
  We present a foundation model (FM) for lossy scientific data compression.<n>We combine a variational autoencoder (E) with a hyper-prior structure and a super-resolution (SR) module.
  arXiv  Detail & Related papers  (2024-12-22T22:57:08Z)
• Compression-Realized Deep Structural Network for Video Quality Enhancement [78.13020206633524]
  This paper focuses on the task of quality enhancement for compressed videos. Most of the existing methods lack a structured design to optimally leverage the priors within compression codecs. A new paradigm is urgently needed for a more conscious'' process of quality enhancement.
  arXiv  Detail & Related papers  (2024-05-10T09:18:17Z)
• Convolutional variational autoencoders for secure lossy image compression in remote sensing [47.75904906342974]
  This study investigates image compression based on convolutional variational autoencoders (CVAE) CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets.
  arXiv  Detail & Related papers  (2024-04-03T15:17:29Z)
• Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes [54.18186259484828]
  In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds. We show strong evidences that variable-length is beneficial for compression in FL. We present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response to the dynamics of model updates.
  arXiv  Detail & Related papers  (2024-02-06T07:25:21Z)
• A Survey on Transformer Compression [84.18094368700379]
  Transformer plays a vital role in the realms of natural language processing (NLP) and computer vision (CV) Model compression methods reduce the memory and computational cost of Transformer. This survey provides a comprehensive review of recent compression methods, with a specific focus on their application to Transformer-based models.
  arXiv  Detail & Related papers  (2024-02-05T12:16:28Z)
• SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks [13.706955134941385]
  We propose SRN-SZ, a deep learning-based scientific error-bounded lossy compressor. SRN-SZ applies the most advanced super-resolution network HAT for its compression. In experiments, SRN-SZ achieves up to 75% compression ratio improvements under the same error bound.
  arXiv  Detail & Related papers  (2023-09-07T22:15:32Z)
• Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder [8.759778406741276]
  This work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called textitBicephalous Convolutional AutoEncoder (BCAE) It shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP.
  arXiv  Detail & Related papers  (2021-11-09T21:26:37Z)
• Exploring Autoencoder-based Error-bounded Compression for Scientific Data [14.724393511470225]
  We develop an error-bounded autoencoder-based framework in terms of the SZ model. We optimize the compression quality for the main stages in our designed AE-based error-bounded compression framework.
  arXiv  Detail & Related papers  (2021-05-25T07:53:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.