A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved tree-structured codebook search algorithm for grayscale image compression
Fundamenta Informaticae
Fast codebook search algorithms based on tree-structured vector quantization
Pattern Recognition Letters
A Fast VQ Codebook Generation Algorithm Based on Otsu Histogram Threshold
Fundamenta Informaticae
Vector quantization using the firefly algorithm for image compression
Expert Systems with Applications: An International Journal
Density-based image vector quantization using a genetic algorithm
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
A Fast VQ Codebook Generation Algorithm Based on Otsu Histogram Threshold
Fundamenta Informaticae
An Improved Tree-Structured Codebook Search Algorithm for Grayscale Image Compression
Fundamenta Informaticae
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Vector quantization (VQ) is a fundamental technique for image compression. But it takes time to search for a similar codeword in a codebook. Thus, the codebook search is one of the major bottlenecks in VQ. We propose a new search algorithm which is used to speed up both the codebook generation and the encoding. We call it the diagonal axes method (DAM). This new algorithm contains two major techniques: diagonal axes analysis (DAA) and orthogonal checking (OC). Since most of these procedures simply involve additions and subtractions, DAM is more efficient than some other related algorithms. Simulation results confirm this effectiveness