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Tytuł artykułu

Data Compressor for VQ Index Tables

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The vector quantization (VQ) compression scheme has been well accepted as an efficient image compression technique. However, the compression bit rate of the VQ scheme is limited. In order to improve its efficiency, in this paper, we shall propose a new lossless data compression scheme to further condense the VQ index table. The proposed scheme exploits the inter-block correlations in the index table to re-encode the indices. Unlike the well known existing re-encoding schemes such as SOC and STC, the proposed scheme uses a smaller number of compression codes to encode every index that coincides with another on the predefined path. Compared with VQ, SOC and STC, the proposed scheme performs better in terms of compression bit rate.
Słowa kluczowe
Wydawca
Rocznik
Strony
353--371
Opis fizyczny
Bibliogr. 20 poz., fot., tab.
Twórcy
autor
  • Department of Computer Science and Information Engineering, National Chung Cheng Uniersity, Chiayi, Taiwan, 621, R.O.C.
autor
  • Department of Computer Science and Information Engineering, National Chung Cheng Uniersity, Chiayi, Taiwan, 621, R.O.C.
Bibliografia
  • [1] Faller, N.: An Adaptive System for Data Compression, Proceedings of the 7th Asilomar Conference on Circuits, Systems, and Computers, 1973, 593–597.
  • [2] Gallagher, R. G.: Variations on a Theme by Huffman, IEEE Transactions on Information Theory, IT-24(6), 1978, 668–674.
  • [3] Gersho, A. and Gray, R. M.: Vector Quantization and Signal Compression, Kluwer Academic Publishers, Boston, MA, 1992.
  • [4] Hartenstein, H., Ruhl, M. and Saupe, D.: Region-Based Fractal Image Compression, IEEE Transactions on Image Processing, 9(7), 2000, 1171–1184.
  • [5] Hsieh, C. H. and Shue, J. S.: Frame Adaptive Finite-State Vector Quantization for Image Sequence Coding, Image Communications, 7, 1995, 13–26.
  • [6] Hsieh, C. H. and Tsai, J. C.: Lossless Compression of VQ Index with Search-order Coding, IEEE Transactions on Image Processing, 5(11), 1996, 1579–1582.
  • [7] Hsieh, C. H., Tsai, J. C. and Lu, P. C.: Noiseless Coding of VQ Index Using Index Grouping Algorithm, IEEE Transactions on Communications, 44(12), 1996, 1643–1648.
  • [8] Huang, C. M., Bi, Q., Stiles, G. S. and Harris, R. W.: Fast Full-Search Equivalent Encoding Algorithms for Image Compression Using Vector Quantization, IEEE Transactions on Image Processing, 1(3), 1992, 413–416.
  • [9] Knuth, D. E.: Dynamic Huffman Coding, Journal of Algorithms, 6, 1985, 163–180.
  • [10] Linde, Y., Buzo, A. and Gray, R. M.: An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, 28(1), 1980, 84–95.
  • [11] Lo, K. T. and Feng, J.: PredictiveMean Search Algorithms for Fast VQ Encoding of Images, IEEE Transactions on Consumer Electronics, 41(2), 1995, 327–331.
  • [12] Mongkolworaphol, S., Rangsanseri, Y. and Thitimajshima, P.: Multispectral Image Compression Using FCM-Based Vector Quantization, Proceedings of the 21st Asian Conference on Remote Sensing, Taipei, Taiwan, 2000.
  • [13] Nasrabadi, N. M. and King, R. B.: Image Coding Using Vector Quantization: A Review, IEEE Transactions on Communications, 36(8), 1988, 957–971.
  • [14] Pennebaker, W. B. and Mitchell, J. L.: JPEG - Still Image Data Compression Standards, Van Nostrand Reinhold, New York, 1993.
  • [15] Rao, K. R. and Yip, P.: Discrete Cosine Transforms - Algorithms, Advantages, Applications, Academic Press, London, UK, 1990.
  • [16] Ramasubramanian, V. and Paliwal, K.: Fast K-dimensional Tree Algorithms for Nearest Neighbor Search with Application to Vector Quantization Encoding, IEEE Transactions on Signal Processing, 40(3), 1992, 518–531.
  • [17] Sayood, K. andMemon, N. D.: Lossless Compression, Communication Handbook, 2nd Edition, J. D. Gibson Ed., CRC Press, 2002.
  • [18] Sheu, M. H, Tsai, S. C. and Shieh, M. D.: A Lossless Index Coding Algorithm and VLSI Design for Vector Quantization, Proceedings of the 10th VLSI/CAD Symposium, 1997, 485–488.
  • [19] Shanbehzadeh, J. and Ogunbona, P. O.: Index-Compressed Vector Quantization Based on Index Mapping, IEE Proceedings - Vision, Image and Signal Processing, 144, 1997, 31–38.
  • [20] Tzou, K. H.: High-order Entropy Coding for Images, IEEE Transactions on Circuit Systems Video Technology, 2, 1992, 87–89.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0007-0017
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