An improved tree-structured codebook search algorithm for grayscale image compression
Fundamenta Informaticae
High-capacity image hiding scheme based on vector quantization
Pattern Recognition
Full-Searching-Equivalent Vector Quantization Using Two-Bounds Triangle Inequality
Fundamenta Informaticae
Fast VQ codebook search algorithm for grayscale image coding
Image and Vision Computing
Predictive Grayscale Image Coding Scheme Using VQ and BTC
Fundamenta Informaticae
Block Prediction Vector Quantization for Grayscale Image Compression
Fundamenta Informaticae
A novel approach to compress image set
ISPRA'05 Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation
New Bit Reduction of Vector Quantization Using Block Prediction and Relative Addressing
Fundamenta Informaticae
Fast VQ Codebook Generation Method Using Codeword Stability Check and Finite State Concept
Fundamenta Informaticae
Image and Vision Computing
High capacity SMVQ-based hiding scheme using adaptive index
Signal Processing
An adaptive image authentication scheme for vector quantization compressed image
Journal of Visual Communication and Image Representation
New Bit Reduction of Vector Quantization Using Block Prediction and Relative Addressing
Fundamenta Informaticae
Fast VQ Codebook Generation Method Using Codeword Stability Check and Finite State Concept
Fundamenta Informaticae
Full-Searching-Equivalent Vector Quantization Using Two-Bounds Triangle Inequality
Fundamenta Informaticae
Predictive Grayscale Image Coding Scheme Using VQ and BTC
Fundamenta Informaticae
Block Prediction Vector Quantization for Grayscale Image Compression
Fundamenta Informaticae
An Improved Tree-Structured Codebook Search Algorithm for Grayscale Image Compression
Fundamenta Informaticae
| Hi-index | 0.43 |
A fast codebook training algorithm based on the Linde, Buzo and Gray (1980) LBG algorithm is proposed. The fundamental goal of this method is to reduce the computation cost in the codebook training process. In this method, a kind of mean-sorted partial codebook search algorithm is applied to the closest codeword search. At the same time, a generalized integral projection model is developed for the generation of test conditions, which are used to speed up the search process in finding the closest codeword for each training vector. With this proposed method, a significant time reduction can be achieved by avoiding the computation of unnecessary codewords. Our simulation results show that a significant reduction in computation cost is obtained with this proposed method. Besides, this method provides a flexible way of selecting the test conditions to accommodate the different image training sets