{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:03:55Z","timestamp":1754161435711,"version":"3.41.2"},"reference-count":27,"publisher":"Emerald","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,12,20]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used for reconstructing the original data, PCA has not been successfully applied to image compression. To solve this problem, this paper aims to propose a new technique called k-PCA.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>Actually, k-PCA is a combination of vector quantization (VQ) and PCA. The basic idea is to divide the problem space into k clusters using VQ, and then find a PCA encoder for each cluster. The point is that if the k-PCA encoder is obtained using data containing enough information, it can be used as a semi-universal encoder to compress all images in a given domain.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>Although a k-PCA encoder is more complex than a single PCA encoder, the compression ratio can be much higher because the transformation matrices can be excluded from the encoded data. The performance of the k-PCA encoder can be improved further through learning. For this purpose, this paper-proposes an extended LBG algorithm.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>The effectiveness of the k-PCA is demonstrated through experiments with several well-known test images.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/17427370710847327","type":"journal-article","created":{"date-parts":[[2008,1,12]],"date-time":"2008-01-12T07:06:37Z","timestamp":1200121597000},"page":"205-220","source":"Crossref","is-referenced-by-count":1,"title":["<i>k<\/i>-PCA: a semi-universal encoder for image compression"],"prefix":"10.1108","volume":"3","author":[{"given":"Chuanfeng","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Electric and Information Engineering, The Beijing Institute of Technology, People's Republic of China"}]},{"given":"Qiangfu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Multimedia Device Laboratory, The University of Aizu, Aizu-Wakamatsu City, Japan"}]}],"member":"140","reference":[{"key":"2025072819275847300_b1","doi-asserted-by":"crossref","unstructured":"Ahmed, N.\n          , Natarajan, T. and Rao, R.K. (1974), \u201cDiscrete cosine transform\u201d, IEEE Transactions on Computers, C-23, pp. 90-3","DOI":"10.1109\/T-C.1974.223784"},{"key":"2025072819275847300_b2","doi-asserted-by":"crossref","unstructured":"Antonini, M.\n          , Barlaud, M., Mathieu, P. and Daubechies, I. (1992), \u201cImage coding using the wavelet transform\u201d, IEEE Transactions on Image Processing, Vol. 1, pp. 205-20.","DOI":"10.1109\/83.136597"},{"key":"2025072819275847300_b3","doi-asserted-by":"crossref","unstructured":"Carrato, S.\n           (1992), \u201cNeural networks for image compression\u201d, in\u2008Gelenbe (Ed.), Neural Networks: Advances and Applications, 2nd ed., North-Holland, Amsterdam, pp. 177-98.","DOI":"10.1016\/B978-0-444-89330-7.50012-0"},{"issue":"1","key":"2025072819275847300_b4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1006\/jvci.1997.0327","article-title":"An improved improved VQ codebook searching using principal component analysis","volume":"8","author":"Chang","year":"1997","journal-title":"Journal of Visual Communication and Image Representation"},{"issue":"1","key":"2025072819275847300_b5","first-page":"9","article-title":"Image sequence coding adaptive tree-structured vector quantization with multipath searching","volume":"139","author":"Chang","year":"1992","journal-title":"IEE Proceedings"},{"key":"2025072819275847300_b6","unstructured":"Ching, Y.C.\n           (1973), \u201cDifferential pulse code modulations system having dual quantization schemes\u201d, US Patent 3781685."},{"key":"2025072819275847300_b7","unstructured":"Dony, R.D.\n           (1995), \u201cAdaptive transform coding of images using a mixture of principal components\u201d, PhD thesis, McMaster University, Hamilton."},{"key":"2025072819275847300_b8","doi-asserted-by":"crossref","unstructured":"Dony, R.D.\n           (1998), \u201cA Comparison of Hebbian learning methods for image compression using the mixture of principal components network\u201d, Proceedings of SPIE, Applications of Artificial Neural Networks in Image Processing III, Vol. 3307, pp. 64-75.","DOI":"10.1117\/12.304660"},{"issue":"2","key":"2025072819275847300_b9","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1109\/TCOM.1982.1095471","article-title":"Vector quantization and predictive quantizers for Gauss-Markov sources","volume":"30","author":"Gray","year":"1982","journal-title":"IEEE Transactions on Communications"},{"key":"2025072819275847300_b10","doi-asserted-by":"crossref","unstructured":"Jolliffe, I.T.\n           (1986), Principal Component Analysis, Springer-Verlag, New York, NY.","DOI":"10.1007\/978-1-4757-1904-8"},{"key":"2025072819275847300_b11","doi-asserted-by":"crossref","unstructured":"Kambhatla, N.\n           and Leen, T.K. (1997), \u201cDimension reduction by local principal component analysis\u201d, Neural Computation, Vol. 9, pp. 1493-516.","DOI":"10.1162\/neco.1997.9.7.1493"},{"key":"2025072819275847300_b12","doi-asserted-by":"crossref","unstructured":"Kung, S.Y.\n           and Diamantaras, K.I. (1990), \u201cA neural network learning algorithm for adaptive principal component extraction (APEX)\u201d, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Alburqurque, NM, 3-6\u2008April, Vol. 90, pp. 861-4.","DOI":"10.1109\/ICASSP.1990.115975"},{"issue":"1","key":"2025072819275847300_b13","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/TCOM.1980.1094577","article-title":"An algorithm for vector quantization","volume":"28","author":"Linde","year":"1980","journal-title":"IEEE Transactions on Communications"},{"key":"2025072819275847300_b14","unstructured":"Lv, C.F.\n           and Zhao, Q.F. (2004), \u201cFractal based VQ image compression algorithm\u201d, Proceedings of the 66th National Convention of IPSJ."},{"key":"2025072819275847300_b15","doi-asserted-by":"crossref","unstructured":"Lv, C.F.\n           and Zhao, Q.F. (2005), \u201cA simplified MPC for image compression\u201d, Proceedings of the International Conference on Computer and Information Technology, Shanghai, pp. 580-4.","DOI":"10.1109\/CIT.2005.51"},{"key":"2025072819275847300_b16","unstructured":"Lv, C.F.\n           (2004), \u201cIFS+VQ: a new method for image compression\u201d, Master's thesis, The University of Aizu, Aizu."},{"key":"2025072819275847300_b17","doi-asserted-by":"crossref","unstructured":"Oja, E.\n           (1982), \u201cA simplified neuron model as a principal component analyzer\u201d, Journal of Mathematics and Biology, Vol. 15, pp. 267-73.","DOI":"10.1007\/BF00275687"},{"key":"2025072819275847300_b18","doi-asserted-by":"crossref","unstructured":"Roweis, S.T.\n           and Saul, L.K. 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(1994), \u201cAn improvement on codebook search for vector quantization\u201d, IEEE Transactions on Communications, Vol. 42 Nos. 2\/3\/4, pp. 208-10.","DOI":"10.1109\/TCOMM.1994.577009"},{"key":"2025072819275847300_b24","doi-asserted-by":"crossref","unstructured":"Fischerm, Y.\n           (1994), Fractal Image Compression, Springer, New York, NY.","DOI":"10.1007\/978-1-4612-2472-3_3"},{"key":"2025072819275847300_b25","unstructured":"ISO\/IEC JTC 1\/SC 29\/WG 1 WD14495, \u201cJPEG LS image coding system\u201d, July, 1996."},{"key":"2025072819275847300_b26","unstructured":"ISO\/IEC WD15444-1, \u201cJPEG 2000 lossless and lossy compression of continuous-tone and bi-level still images\u201d, ISO December 1999."},{"key":"2025072819275847300_b27","doi-asserted-by":"crossref","unstructured":"Lv, C.F.\n           and Zhao, Q.F. 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