In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.

In this letter, a new scheme of designing two-level minimum mean square error quantizer for image coding is proposed. Genetic algorithm is applied to achieve this goal. Comparisons of results with various methods have verified, the proposed method can reach nearly optimal quantization with only less iterations.

In this paper, a new scheme for designing multilevel BTC coding is proposed. Optimal quantization can be obtained by selecting the quantization threshold with an exhaustive search. However, this requires an enormous amount of computation and is, thus impractical when we consider an exhaustive search for the multilevel BTC. In order to find a better threshold so that the average mean square error between the original and reconstructed images is a minimum, the genetic algorithm is applied. Comparison of the results of the proposed method with the exhaustive search reveal that the former method can almost achieve optimal quantization with much less computation than that required in the latter case.

A repeating watermarking technique based on visual secret sharing (VSS) scheme provides the watermark repeated throughout the image for avoiding the image cropping. In this paper, the watermark is divided into public watermark and secret watermark by using the VSS scheme to improve the security of the proposed watermarking technique. Unlike the traditional methods, the original watermark does not have to be embedded into the host image directly and, thus, it is hard to be detected or removed by the pirates or hackers. The retrieved watermark extracted from the watermarked image does not require the complete original image, but requires a secret watermark. Furthermore, the watermarking technique suits the watermark with an adaptive size of binary image for designing the watermarking system. The experimental results show that the proposed method can withstand the common image processing operations, such as filtering, lossy compression and the cropping attacking etc. The embedded watermark is imperceptible, and that the extracted watermark identifies clearly the owner's copyright.