{"ID":2825602,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21433","arxiv_id":"2512.21433","title":"DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction","abstract":"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.","short_abstract":"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 nat...","url_abs":"https://arxiv.org/abs/2512.21433","url_pdf":"https://arxiv.org/pdf/2512.21433v1","authors":"[\"Khondoker Mirazul Mumenin\",\"Robert Underwood\",\"Dong Dai\",\"Jinzhen Wang\",\"Sheng Di\",\"Zarija Lukić\",\"Franck Cappello\"]","published":"2025-12-24T21:46:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\",\"cs.PF\"]","methods":"[]","has_code":false}