Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page 106
... prediction model , we can predict a pixel value which is very close to its actual value . A practical approach to the prediction model is to take a linear com- bination of the previously encoded neighboring pixels . The reason for ...
... prediction model , we can predict a pixel value which is very close to its actual value . A practical approach to the prediction model is to take a linear com- bination of the previously encoded neighboring pixels . The reason for ...
Page 114
... prediction . Table 3.2 Prediction functions in lossless JPEG Option Prediction function 0 No prediction 12 1 Xp = A 2 X , = B Type of prediction Differential Coding 1 - D Horizontal Prediction 1 - D Vertical Prediction 3 Xp = C 1 - D ...
... prediction . Table 3.2 Prediction functions in lossless JPEG Option Prediction function 0 No prediction 12 1 Xp = A 2 X , = B Type of prediction Differential Coding 1 - D Horizontal Prediction 1 - D Vertical Prediction 3 Xp = C 1 - D ...
Page 115
... prediction function . Except for the first line , option 2 is used to predict the very first pixel in all other lines . For all other pixels , we select one of the eight options for prediction function from Table 3.2 . Once a predictor ...
... prediction function . Except for the first line , option 2 is used to predict the very first pixel in all other lines . For all other pixels , we select one of the eight options for prediction function from Table 3.2 . Once a predictor ...
Contents
Soft Computing | 37 |
Multimedia Data Compression | 89 |
standard | 129 |
Copyright | |
9 other sections not shown
Other editions - View all
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya Limited preview - 2005 |
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya No preview available - 2005 |
Common terms and phrases
applications approach association rules ATATA binary Bioinformatics Boyer-Moore algorithm C₁ categorical classification clustering coding coefficients color components computational complexity content-based image retrieval corresponding data compression data mining database dataset decision tree decoder defined dictionary distance document domain efficient encoder outputs entropy entropy encoding evaluation example extracted feature fuzzy sets gene Hence Huffman code IEEE Transactions image retrieval initial input integer involving JPEG Karp-Rabin knowledge discovery knowledge-based network learning length linguistic matching algorithms matrix measure method mismatch Mitra multimedia data neural networks neuro-fuzzy neurons objects occurrence optimal partition pattern matching pixel prediction prefix protein pruning quantization query represented result rough set sample Section sequence shown in Fig soft computing split statistical string matching structure substring suffix symbol Table techniques text mining transformed vector wavelet Web mining weights