An Efficient DCT Compression Technique using Strassen's Matrix Multiplication Algorithm

compression' minimizes the problem that we face in storing and transmitting large amount of data. It reduces the size of data required to represent a digital image. In this procedure, DCT plays an important role. It separates information by using different frequencies. In Discrete Cosine Transformation (DCT), Quantization and encoding are the steps involved in the compression of the JPEG image. In this whole work, while using DCT, we have used Stassen's matrix multiplication algorithm for reducing the complex matrix multiplication problems. As per the result obtained from experiment, the performance of DCT is improved by using Stassen's matrix multiplication algorithm. The performance analysis is carried out through Peak signal to noise ratio (PSNR), and the different compression ratio (CR) for the different images. Keywords— compression, strassen's matrix multiplication, CR, DCT, JPEG, PSNR.

[1]  S. V. Viraktamath,et al.  Performance analysis of JPEG algorithm , 2011, 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies.

[2]  R. Cintra,et al.  Image Compression via a Fast DCT Approximation , 2010, IEEE Latin America Transactions.

[3]  Fan Zhang,et al.  Reduced-Reference Image Quality Assessment Using Reorganized DCT-Based Image Representation , 2011, IEEE Transactions on Multimedia.

[5]  Wei Zheng,et al.  Research in a fast DCT algorithm based on JPEG , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).

[6]  Saad Bouguezel,et al.  An efficient fast integer DCT transform for images compression with 16 additions only , 2011, International Workshop on Systems, Signal Processing and their Applications, WOSSPA.

[7]  Dr. S. Varadarajan,et al.  Image Compression Using Transform Coding Methods , 2007 .

[8]  Zheng Wei,et al.  Analysis of JPEG encoder for image compression , 2011, 2011 International Conference on Multimedia Technology.

[9]  Priyatam Kumar,et al.  Comparative analysis of variable quantization DCT and variable rank matrix SVD algorithms for image compression applications , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[10]  Alessandro Piva,et al.  Detection of non-aligned double JPEG compression with estimation of primary compression parameters , 2011, 2011 18th IEEE International Conference on Image Processing.

[11]  Stefano Tubaro,et al.  The cost of JPEG compression anti-forensics , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Jiang Dalin,et al.  Survey on the technology of image processing based on DCT compressed domain , 2011, 2011 International Conference on Multimedia Technology.

[13]  Aree Ali Mohammed,et al.  Hybrid transform coding scheme for medical image application , 2010, The 10th IEEE International Symposium on Signal Processing and Information Technology.