Optimization of Huffman Encoding for Multiple Images Compression using PCDA
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

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Research Paper | Computer Science & Engineering | India | Volume 5 Issue 6, June 2016

Optimization of Huffman Encoding for Multiple Images Compression using PCDA

Khushboo Kumar Sahu, K. J. Satao

There are different classification and methods of image compression techniques. By using these compression methods we can reduce the redundant data from the file while preserving the same amount of information as it was before the compression. By these methods we can save the lots of storage space required to store the file and also the bandwidth required to send the data over the network. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set. Linear Discriminant analysis (LDA) is a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. In this paper we are using the combination of PCA & LDA as PCDA approach where we compress the multiple images such that the compression ratio is greater than before and time requirement is lesser than it is for these individual images. More compression ratio can be obtained while working with multiple files rather than unitary files.

Keywords: Image Compression, Dimension Reduction, Linear Discriminant Analysis LDA, Principal Component Analysis PCA, Huffman encoding, Compression Technique

Edition: Volume 5 Issue 6, June 2016

Pages: 1049 - 1052

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How to Cite this Article?

Khushboo Kumar Sahu, K. J. Satao, "Optimization of Huffman Encoding for Multiple Images Compression using PCDA", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=NOV164401, Volume 5 Issue 6, June 2016, 1049 - 1052

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