{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:45:23Z","timestamp":1760237123604,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T00:00:00Z","timestamp":1583193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of     2.0272     for DCT,     1.179     for WT and     4.301     for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of     0.1108     for DCT,     0.051     for WT and     0.175     for KLT.<\/jats:p>","DOI":"10.3390\/sym12030378","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T03:24:20Z","timestamp":1583292260000},"page":"378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Chimera: A New Efficient Transform for High Quality Lossy Image Compression"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1681-7379","authenticated-orcid":false,"given":"Walaa","family":"Khalaf","sequence":"first","affiliation":[{"name":"Computer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-2605","authenticated-orcid":false,"given":"Ahmad Saeed","family":"Mohammad","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0142-6914","authenticated-orcid":false,"given":"Dhafer","family":"Zaghar","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,3]]},"reference":[{"key":"ref_1","first-page":"272","article-title":"Image compression techniques-a review","volume":"5","author":"Khobragade","year":"2014","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Taubman, D.S., and Marcellin, M.W. (2002). Image Compression Overview. JPEG2000 Image Compression Fundamentals, Standards and Practice, Springer.","DOI":"10.1007\/978-1-4615-0799-4"},{"key":"ref_3","unstructured":"Cebrail, T., and Sarikoz, S. (July, January 29). An overview of image compression approaches. Proceedings of the 3rd International conference on Digital Telecommunications, Bucharest, Romania."},{"key":"ref_4","first-page":"136","article-title":"Comparison of squeezed convolutional neural network models for eyeglasses detection in mobile environment","volume":"33","author":"Mohammad","year":"2018","journal-title":"J. Comput. Sci. Coll."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, Y., Lin, L., Zheng, W.S., and Liu, X. (2013, January 1\u20138). Human re-identification by matching compositional template with cluster sampling. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.391"},{"key":"ref_6","unstructured":"Mohammad, A.S. (2018). Multi-Modal Ocular Recognition in Presence of Occlusion in Mobile Devices. [Ph.D. Thesis, University of Missouri]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1142\/S0218126606003301","article-title":"Image compression system for mobile communication: Advancement in the recent years","volume":"15","author":"Reaz","year":"2006","journal-title":"J. Circuits Syst. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11554-015-0547-x","article-title":"Overview and evaluation of the JPEG XT HDR image compression standard","volume":"16","author":"Artusi","year":"2019","journal-title":"J. Real Time Image Process."},{"key":"ref_9","first-page":"3145","article-title":"Huffman based LZW lossless image compression using retinex algorithm","volume":"2","author":"Kaur","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_10","first-page":"76","article-title":"Lossless Huffman coding technique for image compression and reconstruction using binary trees","volume":"3","author":"Mathur","year":"2012","journal-title":"Int. J. Comput. Technol. Appl."},{"key":"ref_11","first-page":"3216","article-title":"An approach for Image Compression using Adaptive Huffman Coding","volume":"12","author":"Jagadeesh","year":"2013","journal-title":"Int. J. Eng. Technol. II"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Soh, J.W., Lee, H.S., and Cho, N.I. (2017, January 12\u201315). An image compression algorithm based on the Karhunen Lo\u00e8ve transform. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282257"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khalaf, W., Zaghar, D., and Hashim, N. (2019). Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function. Symmetry, 11.","DOI":"10.3390\/sym11020291"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Khalaf, W., Al Gburi, A., and Zaghar, D. (2019). Pre and Postprocessing for JPEG to Handle Large Monochrome Images. Algorithms, 12.","DOI":"10.3390\/a12120255"},{"key":"ref_15","unstructured":"Lu, T., Le, Z., and Yun, D. (2000, January 28\u201330). Piecewise linear image coding using surface triangulation and geometric compression. Proceedings of the Data Compression Conference, Snowbird, UT, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mukherjee, R., and Chandran, S. (2017, January 2\u20133). Lossy image compression using SVD coding, compressive autoencoders, and prediction error-vector quantization. Proceedings of the 4th International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India.","DOI":"10.1109\/OPTRONIX.2017.8349661"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1007\/s11045-016-0385-4","article-title":"Image modeling based on a 2-D stochastic subspace system identification algorithm","volume":"28","author":"Ramos","year":"2017","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_18","unstructured":"Tourapis, A., and Leontaris, A. (2019). Predictive Motion Vector Coding. (App. 16\/298,051), U.S. Patent."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s11554-018-0800-1","article-title":"Efficient image compression based on side match vector quantization and digital inpainting","volume":"16","author":"Zhou","year":"2019","journal-title":"J. Real Time Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1550012","DOI":"10.1142\/S0219691315500125","article-title":"Empirical evaluation of EZW and other encoding techniques in the wavelet-based image compression domain","volume":"13","author":"Suruliandi","year":"2015","journal-title":"Int. J. Wavelets Multiresolution Inf. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1016\/S0165-1684(00)00035-9","article-title":"Multi-iteration wavelet zero-tree coding for image compression","volume":"80","author":"Losada","year":"2000","journal-title":"Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s10851-007-0057-y","article-title":"A modified embedded zerotree wavelet (MEZW) algorithm for image compression","volume":"30","author":"Ouafi","year":"2008","journal-title":"J. Math. Imaging Vis."},{"key":"ref_23","unstructured":"Rao, K.R., and Yip, P. (2014). Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/0165-1684(88)90095-3","article-title":"Discrete transforms via the Walsh-Hadamard transform","volume":"14","author":"Venkataraman","year":"1988","journal-title":"Signal Process."},{"key":"ref_25","unstructured":"Kondo, H., and Oishi, Y. (2000, January 21\u201325). Digital image compression using directional sub-block DCT. Proceedings of the 2000 International Conference on Communication Technology, Beijing, China."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/3\/378\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:03:30Z","timestamp":1760173410000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/3\/378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,3]]},"references-count":25,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["sym12030378"],"URL":"https:\/\/doi.org\/10.3390\/sym12030378","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,3,3]]}}}