A digital image watermarking technique based on vector quantisation (VQ) is presented. This technique uses codeword indices to carry the watermark information. The technique is secret and efficient, and the watermarked image is robust to VQ compression with the same codebook. The simulation results prove the effectiveness of this technique.

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image qeuality. It proves that such a three-step com bination offers an impressive effectiveness and performance improvement, which is confirmend by simulations involving three image sensors (each of which has a different noise structure).

The classified side-match vector quantizer, CSMVQ, has already been presented for low-bit-rate image en-coding. 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 cnrrcnt input vector itsclf into accounl, This let-ter presents a new CSMVQ in which a two-level block classifier is used to classify input vectors and two diffrent master code-books are used for generating the state codebook according to the variance of the input vcctor. Experimental results prove the cffectivcness of the proposed CSMVQ.

Codeword index assignment (CIA) is a key issue to vector quantization (VQ) In the communication system with channel crrors that will introduce extra distortions in the decoding step. Tabu search algorithm (TSA) has been success-fully used to solve the codeword index assignment for the purpose of minimizing the extra distortions due to blt errors. In this paper, simulated annealing (SA) technique is introduced In the iteration of TSA to Improve the convergence performance, Experimental results show that the modified tabu search algorithm (MTSA) is superior to TSA by evaluating the performance of channel distortion after the same number of iterations.

In this paper, a new and fast encoding algorthm for vector quantization is presenfed. This algorithm makes fall use of two characterisstics of a vector: the sum and the variance. A vector is separated into two subvectors: one is composed of the first half of vector components and the other consists of the remaining vector components. Three inequalities based on the sums and varances of a vector and Its two subyectors components are introduced to rejict those codewords that are impossible to be the nearest code-word, thereby saving a great deal of computational time, while in-troducing no estra distortion compared to the conventional full search algorithm. The simulation results show that the proposed algorithm is faster than the equal-average nearest neighbor search (ENNS), the improved ENNS, the equal-average equal-varlance nearest neighbor search (EENNS)and the improved EENNS algo-rithms. Comparing with the improved EENNS algorithm, the pro-posed algorithm reduces the computational time and the number of distortion calculations by 2.4% to 6%and 20.5% to 26.8%. re-spectively The average improvements of the computational time and the number of distortion calculations are 4% and 24.6% for the codebook sizes of 128 to 1024, respectively.