An improved fast encoding algorithm for vector quantization

In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the technique...

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Vydáno v:	Journal of the American Society for Information Science and Technology Ročník 55; číslo 1; s. 81 - 87
Hlavní autoři:	Liu, Li-Juan, Shen, Xu-Bang, Zou, Xue-Cheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.01.2004 Wiley Wiley Periodicals Inc
Témata:	Academic Achievement Algorithms Applied sciences Artificial intelligence Codes Communications technology Compression Computer mediated communication Computer science; control theory; systems Computer simulation Computerized information storage and retrieval Data compression Data processing Data structures Decomposition Encoding Exact sciences and technology Image compression Image quality Information science Information technology Internet Mathematical models Mathematics Memory Pattern recognition. Digital image processing. Computational geometry Recognition (Achievement) Science Search algorithms Searching Simulation Storage Studies System on chip Telecommunications Vector quantization Wavelet transforms China Vector quantization Image processing Data compression
ISSN:	1532-2882, 2330-1635, 1532-2890, 2330-1643
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Abstract	In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half‐L2‐norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system‐on‐chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms.
AbstractList	In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half-L2-norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system-on-chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms. [Publication Abstract] In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half-L(sub 2)-norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system-on-chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms. (Original abstract In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half‐L 2 ‐norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system‐on‐chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms. In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half-L<2<-norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system-on-chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms. [Publication Abstract] In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half‐L2‐norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system‐on‐chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms.
Author	Zou, Xue-Cheng Liu, Li-Juan Shen, Xu-Bang
Author_xml	– sequence: 1 givenname: Li-Juan surname: Liu fullname: Liu, Li-Juan email: lljhust@163.com organization: Institute of Image Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: Xu-Bang surname: Shen fullname: Shen, Xu-Bang organization: Institute of Image Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China – sequence: 3 givenname: Xue-Cheng surname: Zou fullname: Zou, Xue-Cheng email: estxczou@hust.edu.cn organization: Department of Electronic Science and Technology, Huazhong University of Science and Technology, Wuhan, China
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References	Huang, S.H., & Chen, S.H. (1990). Fast encoding algorithm for VQ-based image coding. Electron Letters, 26, 1618-1619. Wu, K.S., & Lin, J.C. (2000). Fast VQ encoding by an efficient kick-out condition. IEEE Transactions on Circuits and Systems for Video Technology, 10, 59-62. Lee, C.-H., & Chen, L.-H. (1995). A fast search algorithm for vector quantization using mean pyramids of codewords. IEEE Transactions on Communications, 43, 1697-1702. Nasrabadi, N.M., & King, R.A. (1988). Image coding using vector quantization: A review. IEEE Transactions on Communications 36, 957-971. Song, B.C., & Ra, J.B. (2002). A fast search algorithm for vector quantization using L2-norm pyramid of codewords. IEEE Transactions on Image Processing. 11, 10-15. Ra, S.W., & Kim J.K. (1993). A fast mean-distance-ordered partial codebook search algorithm for image vector quantization. IEEE Transactions on Circuits and Systems Part II: Analog and Digital Signal Processing, 40, 576-579. Linde, Y., Buzo, A., & Gray, R.M. (1980). An algorithm for vector quantizer design. IEEE Transactions on Communications COMM-28, 84-95. Li, W., & Salari, E. (1995). A fast vector quantization encoding method for image compression. IEEE Transactions on Circuits and Systems for Video Technology, 5, 119-123. Pan, J.S., Lu, Z.M., & Sun, S.H. (2000). Fast codeword search algorithm for image coding based on mean-variance pyramids of codewords. Electron Letters, 36, 210-211. Baek, S.J., Jeon B., & Sung, K.-M. (1997). Fast encoding algorithm for vector quantzation. IEEE Signal Processing Letters, 4, 325-327. Bei, C.D., & Gray, R.M. (1985). An improvement of the minimum distortion encoding algorithm for vector quantization. IEEE Transactions on Communications, COMM-33, 1132-1133. Lee C.H., & Chen, L.H. (1994). Fast closest codeword search algorithm for vector quantization. IEEE Proceedings Vision and Image Signal Processing, 141, 143-148. Hsieh, C.H., & Liu, Y.J. (2000). Fast search algorithm for vector quantization of image using multiple triangle inequalities and wavelet transform. IEEE Transactions on Image Processing, 9, 321-328. Gray, R.M. (1984). Vector quantization. IEEE Acoustic Speech, Signal Processing Magazine, 1, 4-29. Ramasubramanian, V., & Paliwal, K.K. (1992). An efficient approximation-elimination algorithm for fast nearest-neighbor search based on a spherical distance coordinate formulation. Pattern Recognition Letters 13, 471-480. 1980; COMM‐28 1990; 26 2000; 36 1993; 40 1985; COMM‐33 1984; 1 1994; 141 1988; 36 2000; 10 2000; 9 1995; 43 2002; 11 1992; 13 1997; 4 1995; 5 e_1_2_5_15_1 e_1_2_5_14_1 e_1_2_5_9_1 e_1_2_5_16_1 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_7_1 e_1_2_5_10_1 e_1_2_5_6_1 e_1_2_5_13_1 e_1_2_5_5_1 e_1_2_5_12_1 e_1_2_5_2_1 Bei C.D. (e_1_2_5_3_1) 1985; 33 Gray R.M. (e_1_2_5_4_1) 1984; 1
References_xml	– reference: Bei, C.D., & Gray, R.M. (1985). An improvement of the minimum distortion encoding algorithm for vector quantization. IEEE Transactions on Communications, COMM-33, 1132-1133. – reference: Ra, S.W., & Kim J.K. (1993). A fast mean-distance-ordered partial codebook search algorithm for image vector quantization. IEEE Transactions on Circuits and Systems Part II: Analog and Digital Signal Processing, 40, 576-579. – reference: Pan, J.S., Lu, Z.M., & Sun, S.H. (2000). Fast codeword search algorithm for image coding based on mean-variance pyramids of codewords. Electron Letters, 36, 210-211. – reference: Song, B.C., & Ra, J.B. (2002). A fast search algorithm for vector quantization using L2-norm pyramid of codewords. IEEE Transactions on Image Processing. 11, 10-15. – reference: Lee, C.-H., & Chen, L.-H. (1995). A fast search algorithm for vector quantization using mean pyramids of codewords. 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SubjectTerms	Academic Achievement Algorithms Applied sciences Artificial intelligence Codes Communications technology Compression Computer mediated communication Computer science; control theory; systems Computer simulation Computerized information storage and retrieval Data compression Data processing Data structures Decomposition Encoding Exact sciences and technology Image compression Image quality Information science Information technology Internet Mathematical models Mathematics Memory Pattern recognition. Digital image processing. Computational geometry Recognition (Achievement) Science Search algorithms Searching Simulation Storage Studies System on chip Telecommunications Vector quantization Wavelet transforms
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Title	An improved fast encoding algorithm for vector quantization
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