Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource I, Priya Stella Mary; Siddalingappa, Rashmi; M, Vinay; S, Deepa; P, Margaret Savitha; M, Kannan Title A name given to the resource Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived MethodsX;Volume;15;Issue;;Article No.;103695; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.mex.2025.103695" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.mex.2025.103695</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023898486?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023898486?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant I P.S.M., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; Siddalingappa R., Department of Computer Science and Data Science, York St John University, London, E14 2BA, United Kingdom; M V., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; S D., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; P M.S., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India; M K., Department of Computer Science, CHRIST University, Yeshwanthpur, Bangalore, India Description An account of the resource With the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between SVD and PCA outputs to enhance the quality of reconstructed images.Unlike traditional static fusion techniques, the proposed SFWE deploys a fast bounded scalar optimization strategy so as to dynamically estimate the optimal fusion weights thereby ensuring non-negativity and simplex constraints while significantly reducing computational overhead compared to Sequential Quadratic Programming(SQP) or constrained gradient descent methods.Validation was done across multiple benchmarks datasets namely, USC-SIPI Sequences (grayscale TIFF), Kodak, BSDS500, DRIVE (Digital Retinal Images for Vessel Extraction), and ISPRS Potsdam which cover natural, medical, and remote-sensing images. Per-image processing, runtime measurement, and compressed ratio (CR) were produced automatically by the provided evaluation pipeline;The SFWE method provides greater image quality and structural fidelity across diverse datasets, attaining a PSNR of 40 dB and SSIM of 0.95, outperforming existing approaches such as Discrete Cosine Transform (DCT), Wavelet Transform, Singular Value Decomposition (SVD), and Principal Component Analysis and JPEG2000 + CNN models. In addition, it also maintains a good compression ratio leading to an effective balance between the reduction in file size as well as visual quality of the images, which confirms enhanced structural preservation across diverse image types. To implement a novel ensemble model (SFWE) that optimally balances the outputs of SVD and PCA for doing effective image compression. To achieve a higher SSIM (0.95) and good PSNR (40 dB) compared to compression techniques such as DCT, Wavelet, SVD, PCA, and JPEG2000 + CNN. To ensure adaptive high-quality reconstruction across multiple datasets, demonstrating its suitability for diverse image-intensive applications. 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ Subject The topic of the resource CR; PCA; PSNR; SFWE; SSIM; VD Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 22150161; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online