Fast codeword search algorithm for image coding based on mean-variance pyramids of codewords

2000 ◽  
Vol 36 (3) ◽  
pp. 210 ◽  
Author(s):  
J.S. Pan ◽  
Z.M. Lu ◽  
S.H. Sun
Keyword(s):  
Image Coding ◽  
Search Algorithm ◽  
Mean Variance

scholarly journals Feature Selection in Decision Systems: A Mean-Variance Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chengdong Yang ◽  
Wenyin Zhang ◽  
Jilin Zou ◽  
Shunbo Hu ◽  
Jianlong Qiu
Keyword(s):  
Feature Selection ◽  
Data Clustering ◽  
Search Algorithm ◽  
Evaluation Function ◽  
Uncertainty Measure ◽  
Greedy Search ◽  
Decision Systems ◽  
Mean Variance ◽  
Variance Approach ◽  
Experiment Analysis

Uncertainty measure is an important implement for characterizing the degree of uncertainty. It has been extensively applied in pattern recognition and data clustering. Because of instability of traditional uncertainty measures, mean-variance measure (MVM) is utilized to perform feature selection, which could depress disturbances and noises effectively. Thereby, a novel evaluation function based on MVM is designed. The forward greedy search algorithm (FGSA) with the proposed evaluation function is exploited to perform feature selection. Experiment analysis shows the validity and effectiveness of MVM.

Inter-Image Similarity-Based Fast Adaptive Block Size Vector Quantizer for Image Coding

2017 ◽  
Vol 17 (03) ◽  
pp. 1750017 ◽  
Author(s):  
Ahmed A. Abdelwahab
Keyword(s):  
Vector Quantization ◽  
Image Coding ◽  
Search Algorithm ◽  
Block Size ◽  
Image Similarity ◽  
Bit Rate ◽  
Secret Key ◽  
Design Algorithm ◽  
Block Coding ◽  
Codebook Design

Block coding is well known in the digital image coding literature. Vector quantization and transform coding are examples of well-known block coding techniques. Different images have many similar spatial blocks introducing inter-image similarity. The smaller the block size, the higher the inter-image similarity. In this paper, a new block coding algorithm based on inter-image similarity is proposed where it is claimed that any original image can be reconstructed from the blocks of any other image. The proposed algorithm is simply a vector quantization without the need to a codebook design algorithm and using matrix operations-based fast full search algorithm to find the local minimum root-mean-square error distortion measure to find the most similar code block to the input block. The proposed algorithm is applied in both spatial and transform domains with adaptive code block size. In the spatial domain, the encoding process has fidelity as high as 36.07[Formula: see text]dB with bit rate of 2.22[Formula: see text]bpp, while in the transform domain, the encoded image has good fidelity of 34.94[Formula: see text]dB with bit rate as low as 0.72[Formula: see text]bpp on the average. Moreover, the code image can be used as a secret key to provide secure communications.

Fast VQ codebook search algorithm for grayscale image coding

2008 ◽  
Vol 26 (5) ◽  
pp. 657-666 ◽  
Author(s):  
Yu-Chen Hu ◽  
Bing-Hwang Su ◽  
Chih-Chiang Tsou
Keyword(s):  
Image Coding ◽  
Search Algorithm ◽  
Grayscale Image
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