Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
An Inverse Halftoning Technique Using Modified Look-Up Tables
Fundamenta Informaticae
A fast VQ codebook generation algorithm using codeword displacement
Pattern Recognition
Fast codebook search algorithm for vector quantization using sorting technique
Proceedings of the International Conference on Advances in Computing, Communication and Control
Speed up of the edge-based inverse halftoning algorithm using a finite state machine model approach
Computers & Mathematics with Applications
A novel encoding algorithm for vector quantization using transformed codebook
Pattern Recognition
Image and Vision Computing
Fast agglomerative clustering using information of k-nearest neighbors
Pattern Recognition
Improved inverse halftoning using vector and texture-lookup table-based learning approach
Expert Systems with Applications: An International Journal
An Inverse Halftoning Technique Using Modified Look-Up Tables
Fundamenta Informaticae
| Hi-index | 0.02 |
This correspondence extends and modifies classified vector quantization (CVQ) to solve the problem of inverse halftoning. The proposed process consists of two phases: the encoding phase and decoding phase. The encoding procedure needs a codebook for the encoder which transforms a halftoned image to a set of codeword-indices. The decoding process also requires a different codebook for the decoder which reconstructs a gray-scale image from a set of codeword-indices. Using CVQ, the reconstructed gray-scale image is stored in compressed form and no further compression may be required. This is different from the existing algorithms, which reconstructed a halftoned image in an uncompressed form. The bit rate of encoding a reconstructed image is about 0.51 b/pixel