A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing
Vector quantization and signal compression
Vector quantization and signal compression
A fast branch & bound nearest neighbour classifier in metric spaces
Pattern Recognition Letters
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
Interframe hierarchical address-vector quantization
IEEE Journal on Selected Areas in Communications
Lossless compression of VQ index with search-order coding
IEEE Transactions on Image Processing
Inverse error-diffusion using classified vector quantization
IEEE Transactions on Image Processing
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
Fast-searching algorithm for vector quantization using projection and triangular inequality
IEEE Transactions on Image Processing
Side match and overlap match vector quantizers for images
IEEE Transactions on Image Processing
Predictive classified vector quantization
IEEE Transactions on Image Processing
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In this paper, a novel encoding algorithm for vector quantization is presented. Our method uses a set of transformed codewords and partial distortion rejection to determine the reproduction vector of an input vector. Experimental results show that our algorithm is superior to other methods in terms of the computing time and number of distance calculations. Compared with available approaches, our method can reduce the computing time and number of distance calculations significantly. Compared with the available best method of reducing the number of distance computations, our approach can reduce the number of distance calculations by 32.3-67.1%. Compared with the best encoding algorithm for vector quantization, our method can also further reduce the computing time by 19.7-23.9%. The performance of our method is better when a larger codebook is used and is weakly correlated to codebook size.