In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
[1] Yan Wang; Shoushun Chen; Bermak, A., “FPGA Implementation of
Image Compression using DPCM and FBAR,” in International
Conference on Integrated Circuits, (ISIC '07), 2007, pp. 329–332.
[2] Xiwen OwenZhao, Zhihai HenryHe, “Lossless Image Compression
Using Super-Spatial Structure Prediction,” IEEE Signal Processing
Letters, vol. 17, no. 4, April 2010.
[3] Nelson M. The Data Compression Book. 2nd ed. New York: M&T
Books 1995.
[4] Michael B. Martin and Amy E. Bell, “New Image Compression
Techniques Using Multiwavelets and Multiwavelet Packets,” IEEE
Transactions on Image Processing, vol. 10, no. 4, Apr 2001.
[5] Aaron T. Deever and Sheila S. Hemami, “Lossless Image Compression
With Projection-Based and Adaptive Reversible Integer Wavelet
Transforms,” IEEE Transactions on Image Processing, vol. 12, no. 5,
May 2003.
[6] Nikolaos V. Boulgouris, Dimitrios Tzovaras, and Michael Gerassimos
Strintzis, “Lossless Image Compression Based on Optimal Prediction,
Adaptive Lifting, and Conditional Arithmetic Coding,” IEEE
Transactions on Image Processing, vol. 10, no. 1, Jan 2001.
[7] Eddie Batista de Lima Filho, Eduardo A. B. da Silva Murilo Bresciani
de Carvalho, and Frederico Silva Pinagé, “Universal Image
Compression Using Multiscale Recurrent Patterns With Adaptive
Probability Model,” IEEE Transactions on Image Processing, vol. 17,
no. 4, Apr 2008.
[8] Xin Li and Michael T. Orchard “Edge-Directed Prediction for Lossless
Compression of Natural Images,” IEEE Transactions on Image
Processing, vol. 10, no. 6, Jun 2001.
[9] J. Wang, K.Y. Min, J.W. Chong, “Cost Effective Block Truncation
Coding for Color Image Compression,” Advanced in Information
Sciences and Service Sciences, vol. 2, no. 3, September 2010, pp.91-98.
[10] K.Sowmyan, A.Siddarth, D.Menaka, “A Novel Approach to Image
Compression of Colour Images by Plane Reduction Technique,” World
Academy of Science, Engineering and Technology 81, 2011, pp. 292-
295.
[11] Satish Kumar Singh, Shishir Kumar, “Novel adaptive color space
transform and application to image compression,” Signal Processing:
Image Communication 26, 2011, pp. 662–672.
[12] Arash Abadpour, Shohreh Kasaei, “Color PCA Eigenimages and their
Application to Compression and Watermarking”, submitted to Image &
Vision Computing 21 August 2007.
[13] Davis. L, “Survey of edge detection techniques, computer vision”,
Graph. Image Process, vol. 4, 1975, pp. 248–270.
[14] Canny. J, “A computational approach to edge detection,” IEEE Trans.
Pattern Anal. Machine. Intel., vol. PAMI-8, Nov. 1986, pp. 679–698.
[15] Pennebaker, W.B. Mitchell, J.L. Langdon, G.G. Arps Jr., R. B. (1988),
“An overview of the basic principles of the Q-Coder adaptive binary
arithmetic coder,” IBM Journal of Research and Development, vol. 32,
no. 6, 1988.
[16] Amhamed Saffor, Abdul Rahman Ramli, Kwan-Hoong Ng, “A
Comparative Study of Image Compression between JPEG and Wavelet,”
Malaysian Journal of Computer Science, vol. 14, no. 1, June 2001, pp.
39-45.
[17] Walaa M. Abd-Elhafiez, Wajeb Gharibi, “Color Image Compression
Algorithm Based on DCT Blocks,” International Journal of Computer
Science Issues, IJCSI, vol. 9, Issue 4, July 2012, pp. 323-328.
[18] F. Douak, Redha Benzid, Nabil Benoudjit “Color image compression
algorithm based on the DCT transform combined to an adaptive block
scanning,” Int. J. Electron. Commun. (AEU), vol. 65, 2011, pp. 16–26.
[1] Yan Wang; Shoushun Chen; Bermak, A., “FPGA Implementation of
Image Compression using DPCM and FBAR,” in International
Conference on Integrated Circuits, (ISIC '07), 2007, pp. 329–332.
[2] Xiwen OwenZhao, Zhihai HenryHe, “Lossless Image Compression
Using Super-Spatial Structure Prediction,” IEEE Signal Processing
Letters, vol. 17, no. 4, April 2010.
[3] Nelson M. The Data Compression Book. 2nd ed. New York: M&T
Books 1995.
[4] Michael B. Martin and Amy E. Bell, “New Image Compression
Techniques Using Multiwavelets and Multiwavelet Packets,” IEEE
Transactions on Image Processing, vol. 10, no. 4, Apr 2001.
[5] Aaron T. Deever and Sheila S. Hemami, “Lossless Image Compression
With Projection-Based and Adaptive Reversible Integer Wavelet
Transforms,” IEEE Transactions on Image Processing, vol. 12, no. 5,
May 2003.
[6] Nikolaos V. Boulgouris, Dimitrios Tzovaras, and Michael Gerassimos
Strintzis, “Lossless Image Compression Based on Optimal Prediction,
Adaptive Lifting, and Conditional Arithmetic Coding,” IEEE
Transactions on Image Processing, vol. 10, no. 1, Jan 2001.
[7] Eddie Batista de Lima Filho, Eduardo A. B. da Silva Murilo Bresciani
de Carvalho, and Frederico Silva Pinagé, “Universal Image
Compression Using Multiscale Recurrent Patterns With Adaptive
Probability Model,” IEEE Transactions on Image Processing, vol. 17,
no. 4, Apr 2008.
[8] Xin Li and Michael T. Orchard “Edge-Directed Prediction for Lossless
Compression of Natural Images,” IEEE Transactions on Image
Processing, vol. 10, no. 6, Jun 2001.
[9] J. Wang, K.Y. Min, J.W. Chong, “Cost Effective Block Truncation
Coding for Color Image Compression,” Advanced in Information
Sciences and Service Sciences, vol. 2, no. 3, September 2010, pp.91-98.
[10] K.Sowmyan, A.Siddarth, D.Menaka, “A Novel Approach to Image
Compression of Colour Images by Plane Reduction Technique,” World
Academy of Science, Engineering and Technology 81, 2011, pp. 292-
295.
[11] Satish Kumar Singh, Shishir Kumar, “Novel adaptive color space
transform and application to image compression,” Signal Processing:
Image Communication 26, 2011, pp. 662–672.
[12] Arash Abadpour, Shohreh Kasaei, “Color PCA Eigenimages and their
Application to Compression and Watermarking”, submitted to Image &
Vision Computing 21 August 2007.
[13] Davis. L, “Survey of edge detection techniques, computer vision”,
Graph. Image Process, vol. 4, 1975, pp. 248–270.
[14] Canny. J, “A computational approach to edge detection,” IEEE Trans.
Pattern Anal. Machine. Intel., vol. PAMI-8, Nov. 1986, pp. 679–698.
[15] Pennebaker, W.B. Mitchell, J.L. Langdon, G.G. Arps Jr., R. B. (1988),
“An overview of the basic principles of the Q-Coder adaptive binary
arithmetic coder,” IBM Journal of Research and Development, vol. 32,
no. 6, 1988.
[16] Amhamed Saffor, Abdul Rahman Ramli, Kwan-Hoong Ng, “A
Comparative Study of Image Compression between JPEG and Wavelet,”
Malaysian Journal of Computer Science, vol. 14, no. 1, June 2001, pp.
39-45.
[17] Walaa M. Abd-Elhafiez, Wajeb Gharibi, “Color Image Compression
Algorithm Based on DCT Blocks,” International Journal of Computer
Science Issues, IJCSI, vol. 9, Issue 4, July 2012, pp. 323-328.
[18] F. Douak, Redha Benzid, Nabil Benoudjit “Color image compression
algorithm based on the DCT transform combined to an adaptive block
scanning,” Int. J. Electron. Commun. (AEU), vol. 65, 2011, pp. 16–26.
@article{"International Journal of Information, Control and Computer Sciences:70485", author = "Walaa M.Abd-Elhafiez and Wajeb Gharibi", title = "New Efficient Method for Coding Color Images", abstract = "In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
", keywords = "Image compression, color image, Q-coder,
quantization, edge-detection.", volume = "8", number = "2", pages = "403-5", }
