Keywords : compression


Digital Image Compression using Karhunen-Loève Transform

Ghada Thanoon Younes

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 3, Pages 47-59
DOI: 10.33899/csmj.2013.163535

In this research present the digital image compression using by  Karhunen-Loève Transform (KLT), by convert a color digital  image to a gray square digital image, then select the no. of eigen values and eigen vectors that can reconstruct the image,  that be very near to the original image.
And then calculate compression ratio and a high result reach it, after applied fidelity criteria on image produce from compression represented by (PSNR, MSE, correlation coefficient and compression ratio), and using a matlab language programming for execute this research.
 

Audio File Compression Using Counter Propagation Neural Network

Saja J. Mohammed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 1, Pages 153-168
DOI: 10.33899/csmj.2010.163869

In this paper audio files are compressed using counter propagation neural network (CPNN) which is one of the fastest neural networks in multi media. The utilized counter propagation neural network was trained on uncompressed sound file to obtain the final weights of this CPNN (Kohonen layer, Grossberg layer ).
In compression operation: the sound signal segmented to number of frames equal in size. Then these frames are applied step by step, to the first layer of the neural network(kohonen layer) to obtain some compression results. The decompression operation done by retrieve stored information  in resulted file. This information is applied to second layer of this CPNN (Grosberg layer) which will perform decompression operation and retrieve the original sound file. The proposed algorithm is applied on (.wav) audio files , The results show high performance in addition to short time in compression and decompression operation.
 

Speech Files Compression Based on Signal Feature

Khalil Alsaif; Saja J. Mohammed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 1, Pages 57-79
DOI: 10.33899/csmj.2007.164003

In this research a new algorithm was suggested for compressing speech files added a new style for storing signals, The suggested idea of compression begins with recording the speech via the microphone, then starting the proposed processing steps as follows :

Removing silent period.
Select the number of resulted signal samples.
Segmenting the resulting signal to number of frames.
Applying one of the curves fitting algorithms and obtaining the coefficients for the mathematical representation.
Storing the results in a new file format with .ssc (Speech Signal Compression) extension.

While the decompression process consisted of the reversal compression process steps, the signal is reconstructed using curve fitting coefficients which were stored in the new file, followed by returning the selected sample, then returning the silent period to their original location and finally listening to the retrieved speech signal. When the proposed algorithm had been applied on the files with different speech contents, the compression ratio was approximately (16.283%), and the ratio of SEGSNR was approximately (25.195dB).