{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:55:25Z","timestamp":1781538925607,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302211"],"award-info":[{"award-number":["62302211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62302211"],"award-info":[{"award-number":["62302211"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The rapid growth of scientific spatiotemporal data poses increasing challenges for efficient storage and transmission while preserving sufficient reconstruction fidelity for downstream analysis. Existing compression methods remain limited in this setting: lossless approaches often yield low compression efficiency, conventional lossy methods lack flexible local fidelity control, and learning-based schemes may introduce oversmoothing in reconstructed results. To address these issues, we propose a hybrid machine-learning framework for error-bounded compression of gridded scientific data. The proposed framework integrates MPEG-based temporal coding and transformer-based super-resolution reconstruction to exploit temporal correlation and spatial redundancy, and it introduces an error-bounded correction module to explicitly control local reconstruction errors. In addition, a lightweight Dense Residual Swin Transformer (DRCT)-based reconstruction model is employed to enhance long-range dependency modeling and multi-scale feature recovery. Experimental results on Copernicus Marine Service (CMEMS) gridded sea-level data demonstrate that the proposed framework achieves a favorable balance between compression efficiency and reconstruction quality. For 192\u00d7192 ADT data, the method reaches a peak PSNR of 38.14 dB with a compression ratio of 594\u00d7. With the error-bounded correction module enabled, the reconstructed values are first de-normalized using the recorded normalization parameters, and the local reconstruction error in the original floating-point physical-value domain can then be explicitly controlled by the prescribed absolute error threshold while maintaining a compression ratio of 295\u00d7. Additional experiments on SLA further indicate that the proposed framework is not restricted to a single sea-level variable. These results indicate that the proposed framework is a practical and effective solution for compressing large-scale scientific spatiotemporal data with controllable error and high-fidelity reconstruction.<\/jats:p>","DOI":"10.3390\/computers15060386","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:00:48Z","timestamp":1781535648000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Unified MPEG\u2013Transformer Framework for Error-Bounded Compression and High-Fidelity Reconstruction of Scientific Spatiotemporal Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6350-9895","authenticated-orcid":false,"given":"Zhenyu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and School of Artificial Intelligence, Nanjing Institute of Technology, Nanjing 211167, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s43017-023-00409-w","article-title":"Big Data in Earth System Science and Progress towards a Digital Twin","volume":"4","author":"Li","year":"2023","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e27","DOI":"10.1017\/eds.2024.22","article-title":"Earth System Data Cubes: Avenues for Advancing Earth System Research","volume":"3","author":"Montero","year":"2024","journal-title":"Environ. Data Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"697","DOI":"10.14778\/3574245.3574255","article-title":"Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data","volume":"16","author":"Jiao","year":"2022","journal-title":"Proc. VLDB Endow."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1145\/3733104","article-title":"A Survey on Error-Bounded Lossy Compression for Scientific Datasets","volume":"57","author":"Di","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_5","unstructured":"Azami, N., Fallin, A., and Burtscher, M. (April, January 30). Efficient Lossless Compression of Scientific Floating-Point Data on CPUs and GPUs. Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Rotterdam, The Netherlands."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4440","DOI":"10.1109\/TPDS.2022.3194695","article-title":"Optimizing Error-Bounded Lossy Compression for Scientific Data with Diverse Constraints","volume":"33","author":"Liu","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_7","first-page":"1302","article-title":"TopoSZ: Preserving Topology in Error-Bounded Lossy Compression","volume":"30","author":"Yan","year":"2024","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/TBDATA.2021.3066151","article-title":"High-Ratio Lossy Compression: Exploring the Autoencoder to Compress Scientific Data","volume":"9","author":"Liu","year":"2023","journal-title":"IEEE Trans. Big Data"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lee, J., Gong, Q., Choi, J., Banerjee, T., Klasky, S., Ranka, S., and Rangarajan, A. (2022). Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities. Appl. Sci., 12.","DOI":"10.3390\/app12136718"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6199","DOI":"10.1109\/TPAMI.2024.3378704","article-title":"Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization","volume":"46","author":"Xu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). SwinIR: Image Restoration Using Swin Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, A., Zhang, L., Liu, Y., and Zhu, C. (2023, January 1\u20136). Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01150"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, X., Lee, J., Rangarajan, A., and Ranka, S. (2025). Foundation Model for Lossy Compression of Spatiotemporal Scientific Data. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10\u201313 June 2025, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-981-96-8295-9_27"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","article-title":"Overview of the High Efficiency Video Coding (HEVC) Standard","volume":"22","author":"Sullivan","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hsu, C.C., Lee, C.M., and Chou, Y.S. (2024, January 17\u201318). DRCT: Saving Image Super-Resolution Away from Information Bottleneck. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00618"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TC.2008.131","article-title":"FPC: A High-Speed Compressor for Double-Precision Floating-Point Data","volume":"58","author":"Burtscher","year":"2009","journal-title":"IEEE Trans. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/30.125072","article-title":"The JPEG Still Picture Compression Standard","volume":"38","author":"Wallace","year":"1992","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1109\/TCSVT.2003.815165","article-title":"Overview of the H.264\/AVC Video Coding Standard","volume":"13","author":"Wiegand","year":"2003","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/TVCG.2006.143","article-title":"Fast and Efficient Compression of Floating-Point Data","volume":"12","author":"Lindstrom","year":"2006","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Di, S., and Cappello, F. (2016, January 23\u201327). Fast Error-Bounded Lossy HPC Data Compression with SZ. Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium, Chicago, IL, USA.","DOI":"10.1109\/IPDPS.2016.11"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1109\/TVCG.2014.2346458","article-title":"Fixed-Rate Compressed Floating-Point Arrays","volume":"20","author":"Lindstrom","year":"2014","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chan, K.C.K., Zhou, S., Xu, X., and Loy, C.C. (2022, January 18\u201324). BasicVSR++: Improving Video Super-Resolution With Enhanced Propagation and Alignment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00588"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s13640-019-0465-0","article-title":"Compression Artifacts Reduction by Improved Generative Adversarial Networks","volume":"2019","author":"Zhao","year":"2019","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_27","unstructured":"Ball\u00e9, J., Laparra, V., and Simoncelli, E.P. (2017, January 24\u201326). End-to-End Optimized Image Compression. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_28","unstructured":"Ball\u00e9, J., Minnen, D., Singh, S., Hwang, S.J., and Johnston, N. (May, January 30). Variational Image Compression with a Scale Hyperprior. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_29","unstructured":"Minnen, D., Ball\u00e9, J., and Toderici, G.D. (2018, January 3\u20138). Joint Autoregressive and Hierarchical Priors for Learned Image Compression. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TBDATA.2022.3201176","article-title":"SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors","volume":"9","author":"Liang","year":"2023","journal-title":"IEEE Trans. Big Data"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101590","DOI":"10.1016\/j.softx.2023.101590","article-title":"MGARD: A Multigrid Framework for High-Performance, Error-Controlled Data Compression and Refactoring","volume":"24","author":"Gong","year":"2023","journal-title":"SoftwareX"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Underwood, R., Di, S., Calhoun, J.C., and Cappello, F. (2021, January 14). Productive and Performant Generic Lossy Data Compression with LibPressio. Proceedings of the 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7), St. Louis, MO, USA.","DOI":"10.1109\/DRBSD754563.2021.00005"},{"key":"ref_33","unstructured":"Copernicus Marine Service (2026, June 09). Global Ocean Gridded L4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing. Available online: https:\/\/data.marine.copernicus.eu\/product\/SEALEVEL_GLO_PHY_L4_MY_008_047\/description."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/6\/386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:17:10Z","timestamp":1781536630000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/6\/386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["computers15060386"],"URL":"https:\/\/doi.org\/10.3390\/computers15060386","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]}}}