# DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction

arXiv: https://arxiv.org/abs/2512.21433 · HTML: https://mlsanity.com/paper/2512.21433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Understanding Robustness of Transformers for Image Classification](https://mlsanity.com/paper/2103.14586/) (2021)
- [From Sparse to Soft Mixtures of Experts](https://mlsanity.com/paper/2308.00951/) (2023)
- [A Survey on Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2407.06204/) (2024)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [TTHRESH: Tensor Compression for Multidimensional Visual Data](https://mlsanity.com/paper/1806.05952/) (2018)
- [SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors](https://mlsanity.com/paper/2111.02925/) (2021)
- [Do We Need More Training Data?](https://mlsanity.com/paper/1503.01508/) (2015)
- [High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation](https://mlsanity.com/paper/2311.12133/) (2023)
- [SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution](https://mlsanity.com/paper/2406.03359/) (2024)
- [Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP](https://mlsanity.com/paper/1806.08901/) (2018)
- [Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5](https://mlsanity.com/paper/2206.14761/) (2022)
- [Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling](https://mlsanity.com/paper/2111.09815/) (2021)
- [SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets](https://mlsanity.com/paper/2201.13020/) (2022)
- [TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations](https://mlsanity.com/paper/2301.01901/) (2023)
- [Black-Box Statistical Prediction of Lossy Compression Ratios for Scientific Data](https://mlsanity.com/paper/2305.08801/) (2023)

## Abstract (arXiv)

Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific simulations and instruments. Nevertheless, assessing data quality post-compression remains computationally expensive due to the intensive nature of metric calculations. In this work, we present a general-purpose deep-surrogate framework for lossy compression quality prediction (DeepCQ), with the following key contributions: 1) We develop a surrogate model for compression quality prediction that is generalizable to different error-bounded lossy compressors, quality metrics, and input datasets; 2) We adopt a novel two-stage design that decouples the computationally expensive feature-extraction stage from the light-weight metrics prediction, enabling efficient training and modular inference; 3) We optimize the model performance on time-evolving data using a mixture-of-experts design. Such a design enhances the robustness when predicting across simulation timesteps, especially when the training and test data exhibit significant variation. We validate the effectiveness of DeepCQ on four real-world scientific applications. Our results highlight the framework's exceptional predictive accuracy, with prediction errors generally under 10% across most settings, significantly outperforming existing methods. Our framework empowers scientific users to make informed decisions about data compression based on their preferred data quality, thereby significantly reducing I/O and computational overhead in scientific data analysis.