Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.

This letter proposes a fast k nearest neighbors search algorithm based on the wavelet transform. This technique exploits the important information of the approximation coefficients of the transform coefficient vector, from which we obtain two crucial inequalities that can be used to reject those vectors for which it is impossible to be k nearest neighbors. The computational complexity for searching for k nearest neighbors can be largely reduced. Experimental results on texture classification verify the effectiveness of our algorithm.

A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.

This paper describes the evaluation of a fiber-optic reflective displacement sensor that is compensated for variations in light source intensity, pressure, temperature and opacity of ambient medium. Additionally, the distance information is averaged over several points on the target surface, which reduces signal fluctuations due to inhomogeneities. Furthermore, a practical optical fiber reflective sensor model of measuring oil film thickness for thrust bearing is set up in this paper. Actual measurements were made with HEC 3000 tons' thrust bearing and the results were in good agreement with theoretical calculations.

This Letter presents a novel VQ-based digital image watermarking method. By modifying the conventional GLA algorithm, a codeword-labeled codebook is first generated. Each input image block is then reconstructed by the nearest codeword whose label is equal to the watermark bit. The watermark extraction can be performed blindly. Simulation results show that the proposed method is robust to JPEG compression, vector quantization (VQ) compression and some spatial-domain processing operations.

This letter presents a speech watermarking scheme that is combined with CELP (Code Excited Linear Prediction) speech coding for speech authentication. The excitation codebook of CELP is partitioned into three parts and labeled '0', '1' and 'any' according to the private key. Watermark embedding process chooses the codebook whose label is the same as the watermark bit and combines it with the codebook labeled 'any' for CELP coding. A statistical method is employed to detect the watermark, and the watermark length for authentication and detection threshold are determined by false alarm probability and missed detection probability. The new codebook partition technique produces less distortion, and the statistical detection method guarantees that the error probability can be controlled under prescribed level.

A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.

The classified side-match vector quantizer, CSMVQ, has already been presented for low-bit-rate image encoding. It exploits a block classifier to decide which class the input vector belongs to using the variances of the upper and left codewords. However, this block classifier doesn't take the variance of the current input vector itself into account. This letter presents a new CSMVQ in which a two-level block classifier is used to classify input vectors and two different master codebooks are used for generating the state codebook according to the variance of the input vector. Experimental results prove the effectiveness of the proposed CSMVQ.

This paper presents an image watermarking method for two purposes: to notify the copyright owner with a visible watermark, and to protect the copyright with an invisible watermark. These two watermarks are embedded in different blocks with different methods. Simulation results show that the visible watermark is hard to remove and the invisible watermark is robust.

A novel rough neural network (RNN) structure and its application are proposed in this paper. We principally introduce its architecture and training algorithms: the genetic training algorithm (GA) and the tabu search training algorithm (TSA). We first compare RNN with the conventional NN trained by the BP algorithm in two-dimensional data classification. Then we compare RNN with NN by the same training algorithm (TSA) in functional approximation. Experiment results show that the proposed RNN is more effective than NN, not only in computation time but also in performance.

This Letter presents a fast codeword search algorithm based on ordered Hadamard transform. Before encoding, the ordered Hadamard transform is performed offline on all codewords. During the encoding process, the ordered Hadamard transform is first performed on the input vector, and then a new inequality based on characteristic values of transformed vectors is used to reject the unlikely transformed codewords. Experimental results show that the algorithm outperforms many newly presented algorithms in the case of high dimensionality, especially for high-detail images.