Use of Fuzzy Edge Image in Block Truncation Coding for Image Compression
An image compression method has been developed
using fuzzy edge image utilizing the basic Block Truncation Coding
(BTC) algorithm. The fuzzy edge image has been validated with
classical edge detectors on the basis of the results of the well-known
Canny edge detector prior to applying to the proposed method. The
bit plane generated by the conventional BTC method is replaced with
the fuzzy bit plane generated by the logical OR operation between
the fuzzy edge image and the corresponding conventional BTC bit
plane. The input image is encoded with the block mean and standard
deviation and the fuzzy bit plane. The proposed method has been
tested with test images of 8 bits/pixel and size 512×512 and found to
be superior with better Peak Signal to Noise Ratio (PSNR) when
compared to the conventional BTC, and adaptive bit plane selection
BTC (ABTC) methods. The raggedness and jagged appearance, and
the ringing artifacts at sharp edges are greatly reduced in
reconstructed images by the proposed method with the fuzzy bit
plane.
[1] Graham D. Image Transmission by Two-dimensional Contour Coding.
Proc IEEE 1967; 55(3): 336-346.
[2] Kocher M, Kunt M. Image Data Compression by Contour Texture
Modeling. SPIE Int Conf on the Applications of Digital Image
Processing, Geneva, Switzerland, April 1983: 131-139.
[3] Carlsson S. Sketch based Coding of Grey level Images. Signal
Processing 1988; 15: 57-83.
[4] Cheng SC, Tsai WH. Image Compression by Moment-Preserving Edge
Detection. Pattern Recognition1994; 27(11): 1439-1449.
[5] Desai UY, Mizuki MM, Masaki I, Horn BKP. Edge and Mean Based
Compression. MIT Artificial Intelligence Lab A I Memo No.1584,
November 1996.
[6] Aggoun A, Mabrouk AE. Image Compression Algorithm using Local
Edge Detection. IEEE COMSOC/EURASIP First International
Workshop on Wireless Image/Video Communications, September 1996.
[7] Han WY, Lin JC. Edge Detection and Edge Preserved Compression for
Error-Diffused Images. Comput and Graphics 1997; 21(6): 757-767.
[8] Neves SR, Mendonca GV. Image Coding Based on Edges and Textures
via Wavelet Transform. Proc of the Int Conf on Acoustics, Speech, and
Signal processing 1998; 5: 2689-2692.
[9] Ryu H, Miyanaga Y, Tochinai K. Self-Organized Edge Detection for
Image Compression. IEEE Int Symp on Circuits and Systems, Geneva,
Switzerland, May 2000: 625-628.
[10] Yang CK, Tsai WH. Improving Block Truncation Coding by Line and
Edge Information and Adaptive Bit Plane Selection for Gray-Scale
Image Compression. Pattern Recognition Letters 1995; 16: 67-75.
[11] Delp EJ, Mitchell OR. Image Compression using Block Truncation
Coding. IEEE Transactions on Communications 1979; 27(9): 1335-
1342.
[12] Chan KW, Chan KL. Optimization of Multi-Level Block truncation
Coding. Signal Processing: Image Communication 2001; 16: 445-459.
[13] Franti P, Nevalainen O, Kaukoranta T. Compression of Digital Images
by Block Truncation Coding: A Survey, The Computer Journal, Vol. 37,
No. 4, 1994.
[14] Mitchell HB, Zilverberg N, Avraham M. A Comparison of Different
Block Truncation Coding Algorithms for Image Compression. Signal
Processing: Image Communication 1994; 6: 77-82.
[15] Liang LR, Looney CG. Competitive Fuzzy Edge Detection. Applied
Soft Computing 2003; 3: 123-137.
[16] Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and machine
Vision (2nd Edn.), Thomson Brooks/Cole; 1999.
[17] Kuo YH, Lee CS, Liu CC. A New Fuzzy Edge Detection Method for
Image Enhancement. Sixth IEEE Int Conf on Fuzzy Systems 1997; 2:
1069-1074.
[18] Tizhoosh HR. Fast Fuzzy Edge Detection. Proceedings of the North
American Fuzzy Information Processing Society (IEEE - NAFIPS),
June 2002: 239-242.
[19] Prieto MS, Allen AR. A Similarity Metric for Edge Images. IEEE Trans
on Pattern Analysis and Machine Intelligence 2003; 25(10): 1265-1273.
[1] Graham D. Image Transmission by Two-dimensional Contour Coding.
Proc IEEE 1967; 55(3): 336-346.
[2] Kocher M, Kunt M. Image Data Compression by Contour Texture
Modeling. SPIE Int Conf on the Applications of Digital Image
Processing, Geneva, Switzerland, April 1983: 131-139.
[3] Carlsson S. Sketch based Coding of Grey level Images. Signal
Processing 1988; 15: 57-83.
[4] Cheng SC, Tsai WH. Image Compression by Moment-Preserving Edge
Detection. Pattern Recognition1994; 27(11): 1439-1449.
[5] Desai UY, Mizuki MM, Masaki I, Horn BKP. Edge and Mean Based
Compression. MIT Artificial Intelligence Lab A I Memo No.1584,
November 1996.
[6] Aggoun A, Mabrouk AE. Image Compression Algorithm using Local
Edge Detection. IEEE COMSOC/EURASIP First International
Workshop on Wireless Image/Video Communications, September 1996.
[7] Han WY, Lin JC. Edge Detection and Edge Preserved Compression for
Error-Diffused Images. Comput and Graphics 1997; 21(6): 757-767.
[8] Neves SR, Mendonca GV. Image Coding Based on Edges and Textures
via Wavelet Transform. Proc of the Int Conf on Acoustics, Speech, and
Signal processing 1998; 5: 2689-2692.
[9] Ryu H, Miyanaga Y, Tochinai K. Self-Organized Edge Detection for
Image Compression. IEEE Int Symp on Circuits and Systems, Geneva,
Switzerland, May 2000: 625-628.
[10] Yang CK, Tsai WH. Improving Block Truncation Coding by Line and
Edge Information and Adaptive Bit Plane Selection for Gray-Scale
Image Compression. Pattern Recognition Letters 1995; 16: 67-75.
[11] Delp EJ, Mitchell OR. Image Compression using Block Truncation
Coding. IEEE Transactions on Communications 1979; 27(9): 1335-
1342.
[12] Chan KW, Chan KL. Optimization of Multi-Level Block truncation
Coding. Signal Processing: Image Communication 2001; 16: 445-459.
[13] Franti P, Nevalainen O, Kaukoranta T. Compression of Digital Images
by Block Truncation Coding: A Survey, The Computer Journal, Vol. 37,
No. 4, 1994.
[14] Mitchell HB, Zilverberg N, Avraham M. A Comparison of Different
Block Truncation Coding Algorithms for Image Compression. Signal
Processing: Image Communication 1994; 6: 77-82.
[15] Liang LR, Looney CG. Competitive Fuzzy Edge Detection. Applied
Soft Computing 2003; 3: 123-137.
[16] Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and machine
Vision (2nd Edn.), Thomson Brooks/Cole; 1999.
[17] Kuo YH, Lee CS, Liu CC. A New Fuzzy Edge Detection Method for
Image Enhancement. Sixth IEEE Int Conf on Fuzzy Systems 1997; 2:
1069-1074.
[18] Tizhoosh HR. Fast Fuzzy Edge Detection. Proceedings of the North
American Fuzzy Information Processing Society (IEEE - NAFIPS),
June 2002: 239-242.
[19] Prieto MS, Allen AR. A Similarity Metric for Edge Images. IEEE Trans
on Pattern Analysis and Machine Intelligence 2003; 25(10): 1265-1273.
@article{"International Journal of Electrical, Electronic and Communication Sciences:64304", author = "Amarunnishad T.M. and Govindan V.K. and Abraham T. Mathew", title = "Use of Fuzzy Edge Image in Block Truncation Coding for Image Compression", abstract = "An image compression method has been developed
using fuzzy edge image utilizing the basic Block Truncation Coding
(BTC) algorithm. The fuzzy edge image has been validated with
classical edge detectors on the basis of the results of the well-known
Canny edge detector prior to applying to the proposed method. The
bit plane generated by the conventional BTC method is replaced with
the fuzzy bit plane generated by the logical OR operation between
the fuzzy edge image and the corresponding conventional BTC bit
plane. The input image is encoded with the block mean and standard
deviation and the fuzzy bit plane. The proposed method has been
tested with test images of 8 bits/pixel and size 512×512 and found to
be superior with better Peak Signal to Noise Ratio (PSNR) when
compared to the conventional BTC, and adaptive bit plane selection
BTC (ABTC) methods. The raggedness and jagged appearance, and
the ringing artifacts at sharp edges are greatly reduced in
reconstructed images by the proposed method with the fuzzy bit
plane.", keywords = "Image compression, Edge detection, Ground truth
image, Peak signal to noise ratio", volume = "2", number = "11", pages = "2630-7", }