Neural Networks
Combining Image Compression and Classification Using Vector Quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation by histogram thresholding using hierarchical cluster analysis
Pattern Recognition Letters
An adaptive incremental LBG for vector quantization
Neural Networks
Computer Vision and Image Understanding
Vector quantization of image subbands: a survey
IEEE Transactions on Image Processing
Fast full search equivalent encoding algorithms for image compression using vector quantization
IEEE Transactions on Image Processing
Diagonal axes method (DAM): a fast search algorithm for vector quantization
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
In vector quantization, the codebook generation problem can be formulated as a classification problem of dividing N$_{p}$ training vectors into N$_{c}$ clusters, where N$_{p}$ is the training size of input vectors and N$_{c}$ is the codeword size of codebook. For large N$_{p}$ and N$_{c}$, a traditional search algorithmsuch as the LBG method can hardly find the global optimal classification and needs a great deal of calculation. In this paper, a novel VQ codebook generation method based on Otsu histogram threshold is proposed. The computational complexity of squared Euclidean distance can be reduced to O(N$_{p}$ log$_{2}$ N$_{c}$) for a codebook with gray levels. Our method provides better image quality than recent proposed schemes in high compression ratio. The experimental results and the comparisons show that this method can not only reduce the computational complexity of squared Euclidean distance but also find better codewords to improve the quality of the resulted VQ codebook.