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Zhen Zhang - One of the best experts on this subject based on the ideXlab platform.

  • An on-line universal lossy data compression algorithm via continuous codebook refinement .III. Redundancy analysis
    IEEE Transactions on Information Theory, 1998
    Co-Authors: En-hui Yang, Zhen Zhang
    Abstract:

    For pt.II see ibid., vol.42, p.822-36 (1996). The Gold-washing data compression algorithm is an adaptive Vector quantization algorithm with Vector Dimension n. In this paper, a redundancy problem of the Gold-washing data compression algorithm is considered. It is demonstrated that for any memoryless source with finite alphabet A and generic distribution p and for any R>0, the redundancy of the Gold-washing data compression algorithm with Dimension n (defined as the difference between the average performance of the algorithm and the distortion-rate function D(p,R) of p) is upper-bounded by |/sub /spl delta/R///sup /spl delta//D(p,R)|((|A|+2/spl xi/+4 log n)/2n)+/spl sigma/(logn/n) where /sub /spl delta/R///sup /spl delta//D(p,R) is the partial derivative of D(p,R) with respect to R, |A| is the cardinality of A, and /spl xi/>0 is a parameter used to control the threshold in the Gold-washing algorithm. In connection with the results of Zhang, Yang, and Wei (see ibid., vol.43, no.1, p.71-91, 1997) on the redundancy of lossy source coding, this shows that the Gold-washing algorithm has the optimal convergence rate among all adaptive finite-state Vector quantizers.

  • an adaptive Vector quantizer based on the gold washing method for image compression
    IEEE Transactions on Circuits and Systems for Video Technology, 1994
    Co-Authors: O T C Chen, B J Sheu, Zhen Zhang
    Abstract:

    The VLSI architecture for an adaptive Vector quantizer is presented. The adaptive Vector quantization method does not require a-priori knowledge of the source statistics and the pre-trained codebook. The codebook is generated on the fly and is constantly updated to capture local textual features of data. The source data are directly compressed without requiring the generation of codebook in a separate pass. The adaptive method is based on backward adaption without any side information. The speed of data compression by using the proposed adaptive method is much faster than that by using the conventional Vector quantization methods. The algorithm is shown to reach the rate distortion function for memoryless sources. In image processing, most smooth regions are matched by the code Vectors and most edge data are preserved by using the block-data interpolation scheme. The VLSI architecture consists of two move-to-front Vector quantizers and an index generator. It explores parallelism in the direction of the codebook size and pipelining in the direction of the Vector Dimension. According to the circuit simulations using the popular SPICE program, the computation power of the move-to-front Vector quantizer can reach 40 billion operations per second at a system clock of 100 MHz by using 0.8 /spl mu/m CMOS technology. It can provide a computing capability of 50 Mpixels per second for high-speed image compression. The proposed algorithm and architecture can lead to the development of a high-speed image compressor with great local adaptivity, minimized complexity, and fairly good compression ratio. >

Ick Hoon Jang - One of the best experts on this subject based on the ideXlab platform.

  • content based image retrieval using multiresolution color and texture features
    IEEE Transactions on Multimedia, 2008
    Co-Authors: Young Deok Chun, Ick Hoon Jang
    Abstract:

    In this paper, we propose a content-based image retrieval method based on an efficient combination of multiresolution color and texture features. As its color features, color autocorrelo- grams of the hue and saturation component images in HSV color space are used. As its texture features, BDIP and BVLC moments of the value component image are adopted. The color and texture features are extracted in multiresolution wavelet domain and combined. The Dimension of the combined feature Vector is determined at a point where the retrieval accuracy becomes saturated. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature Vector Dimension is not higher than those of the latter for six test DBs. Especially, it demonstrates more excellent retrieval accuracy for queries and target images of various resolutions. In addition, the proposed method almost always shows performance gain in precision versus recall and in ANMRR over the other methods.

Young Deok Chun - One of the best experts on this subject based on the ideXlab platform.

  • content based image retrieval using multiresolution color and texture features
    IEEE Transactions on Multimedia, 2008
    Co-Authors: Young Deok Chun, Ick Hoon Jang
    Abstract:

    In this paper, we propose a content-based image retrieval method based on an efficient combination of multiresolution color and texture features. As its color features, color autocorrelo- grams of the hue and saturation component images in HSV color space are used. As its texture features, BDIP and BVLC moments of the value component image are adopted. The color and texture features are extracted in multiresolution wavelet domain and combined. The Dimension of the combined feature Vector is determined at a point where the retrieval accuracy becomes saturated. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature Vector Dimension is not higher than those of the latter for six test DBs. Especially, it demonstrates more excellent retrieval accuracy for queries and target images of various resolutions. In addition, the proposed method almost always shows performance gain in precision versus recall and in ANMRR over the other methods.

Sang Uk Lee - One of the best experts on this subject based on the ideXlab platform.

  • on the transformed entropy constrained Vector quantizers employing mandala block for image coding
    Signal Processing-image Communication, 1995
    Co-Authors: Jong Seok Lee, Rin Chul Kim, Sang Uk Lee
    Abstract:

    Abstract This paper presents two image coding techniques employing an entropy constrained Vector quantizer (ECVQ) in the DCT domain. In our approach, the transformed image is rearranged into the Mandala blocks for Vector quantization. Each Mandala block is then divided into several smaller Vectors with variable Dimensions, according to its statistical property, and undergoes the ECVQs which are prepared separately for each Mandala block. While each Mandala block undergoes the unstructured ECVQ in the first technique, the second technique employs a structured ECVQ, i.e., an entropy constrained lattice Vector quantizer (ECLVQ). In the ECLVQ, unlike the conventional lattice VQ combined with entropy coding, we take account of both the distortion and entropy in the encoding. Moreover, in order to improve the performance further, the ECLVQ parameters, including the truncation of a lattice and the scale factor, are optimized according to the input image statistics. Also we reduce the size of the variable word-length code table, which grows exponentially with the Vector Dimension and bit-rate, by grouping the similar codewords. The performances of both techniques are evaluated on the real images, and it is found that the proposed techniques provide 1–2 dB gain over the DCT-classified VQ (Kim and Lee, 1992) in the range of 0.3–0.5 bits per pixel (bpp) and 0.5–1.3 dB gain over the JPEG in the range 0.1–0.5 bpp.

Kenneth Zeger - One of the best experts on this subject based on the ideXlab platform.

  • Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding
    IEEE Transactions on Information Theory, 1994
    Co-Authors: Tamas Linder, Gábor Lugosi, Kenneth Zeger
    Abstract:

    Rate of convergence results are established for Vector quantization. Convergence rates are given for an increasing Vector Dimension and/or an increasing training set size. In particular, the following results are shown for memoryless real-valued sources with bounded support at transmission rate R. (1) If a Vector quantizer with fixed Dimension k is designed to minimize the empirical mean-square error (MSE) with respect to m training Vectors, then its MSE for the true source converges in expectation and almost surely to the minimum possible MSE as O(/spl radic/(log m/m)). (2) The MSE of an optimal k-Dimensional Vector quantizer for the true source converges, as the Dimension grows, to the distortion-rate function D(R) as O(/spl radic/(log k/k)). (3) There exists a fixed-rate universal lossy source coding scheme whose per-letter MSE on a real-valued source samples converges in expectation and almost surely to the distortion-rate function D(R) as O((/spl radic/(loglog n/log n)). (4) Consider a training set of n real-valued source samples blocked into Vectors of Dimension k, and a k-Dimension Vector quantizer designed to minimize the empirical MSE with respect to the m=[n/k] training Vectors. Then the per-letter MSE of this quantizer for the true source converges in expectation and almost surely to the distortion-rate function D(R) as O(/spl radic/(log log n/log n))), if one chooses k=[(1/R)(1-/spl epsiv/)log n] for any /spl epsiv//spl isin/(0.1). >