嵌入式小波零树分块图像编码算法与模拟实现
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摘要
图像是多媒体应用中的重要媒体，而传输频带和存储空间的限制，使得图像压缩技术成为当前信息传输系统中最关键的技术之一。小波变换技术以其良好的空间-频率局部特性和与人眼视觉特性相符的变换机制，在图像编码领域获得了广泛的应用和研究。
针对静止图像的压缩编码，在嵌入式小波零树编码方法的基础上，本文提出了一种具有分块预处理的改进新算法——嵌入式小波零树分块图像编码算法。该方法首先将图像经过小波变换后产生不同分辨率下的各子带图像；然后在阈值T的标准下，对图像水平、垂直和对角线方向的高频子带系数进行四叉树图像分割预处理，从而产生分块类型编码；最后再对均匀图像块进行嵌入式小波零树编码，产生系数类型编码和幅值编码。这种改进的编码算法使得大量非重要系数集中成图像块表示，更好地利用小波变换后的系数，有效地降低码率，同时增强图像压缩后恢复图像的视觉效果。新算法具有与嵌入式小波零树编码相同特点，即可以产生嵌入式码流、不需要训练码本、且在所要求的精度下可以随时结束编码。实验及仿真结果表明，这种改进算法得到的重构图像其主观视觉效果良好，与嵌入式小波零树算法相比，峰值信噪比(PSNR)在相同码率的情况下有较大的提高(平均PSNR值约提高0.37—0.46dB)。
Image is very important medium at application of multimedia. Because of limitation of transmission bandwidth and storage capacity, so image compression became one of critical techniques at information transmission system. In recent decade, the technique of wavelet transform has been applied and studied extensively because of its good features of time-frequency and human visual system.
In this paper, a improved low bit rate image compression algorithm, namely embedded wavelet zero-tree segmntation-based image coding, is put forward about still images based on embedded wavelet zero-tree image coding scheme. First, image is divided subband images of various scales by wavelet transforming. Then at this algorithm the coefficient in horizontal vertical and diagonal orientation sub-images of all scales are quadtree segmented at threshold T criterion. At last these uniform blocks are encoded based on EZW. This improved coding algorithm not only is with the property of EZW but also let unimportant coefficient becoming image blocks to represent so that we can effectively apply the wavelet coefficients of image, low bit-ratio and intensify the visual effective of reconstruction images at the same time. To the embedded zerotree wavelet algorithm(EZW) this paper's coding algorithm is similar the properties which are yielding a fully embedded
code, terminating the encoding and decoding at any point respectively under requirement of rate-constrained or distortion-constrained, absolutely no training and no pre-stored codebooks. Through the experimental and simulated results, we can demonstrate that the reconstruction images are good, and the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved (mean improved PSNR is about 0.37-0.46dB ) compared to embedded zero-tree wavelet encoding (EZW) algorithm at the same bit-ratio.
针对静止图像的压缩编码，在嵌入式小波零树编码方法的基础上，本文提出了一种具有分块预处理的改进新算法——嵌入式小波零树分块图像编码算法。该方法首先将图像经过小波变换后产生不同分辨率下的各子带图像；然后在阈值T的标准下，对图像水平、垂直和对角线方向的高频子带系数进行四叉树图像分割预处理，从而产生分块类型编码；最后再对均匀图像块进行嵌入式小波零树编码，产生系数类型编码和幅值编码。这种改进的编码算法使得大量非重要系数集中成图像块表示，更好地利用小波变换后的系数，有效地降低码率，同时增强图像压缩后恢复图像的视觉效果。新算法具有与嵌入式小波零树编码相同特点，即可以产生嵌入式码流、不需要训练码本、且在所要求的精度下可以随时结束编码。实验及仿真结果表明，这种改进算法得到的重构图像其主观视觉效果良好，与嵌入式小波零树算法相比，峰值信噪比(PSNR)在相同码率的情况下有较大的提高(平均PSNR值约提高0.37—0.46dB)。
Image is very important medium at application of multimedia. Because of limitation of transmission bandwidth and storage capacity, so image compression became one of critical techniques at information transmission system. In recent decade, the technique of wavelet transform has been applied and studied extensively because of its good features of time-frequency and human visual system.
In this paper, a improved low bit rate image compression algorithm, namely embedded wavelet zero-tree segmntation-based image coding, is put forward about still images based on embedded wavelet zero-tree image coding scheme. First, image is divided subband images of various scales by wavelet transforming. Then at this algorithm the coefficient in horizontal vertical and diagonal orientation sub-images of all scales are quadtree segmented at threshold T criterion. At last these uniform blocks are encoded based on EZW. This improved coding algorithm not only is with the property of EZW but also let unimportant coefficient becoming image blocks to represent so that we can effectively apply the wavelet coefficients of image, low bit-ratio and intensify the visual effective of reconstruction images at the same time. To the embedded zerotree wavelet algorithm(EZW) this paper's coding algorithm is similar the properties which are yielding a fully embedded
code, terminating the encoding and decoding at any point respectively under requirement of rate-constrained or distortion-constrained, absolutely no training and no pre-stored codebooks. Through the experimental and simulated results, we can demonstrate that the reconstruction images are good, and the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved (mean improved PSNR is about 0.37-0.46dB ) compared to embedded zero-tree wavelet encoding (EZW) algorithm at the same bit-ratio.
引文
[1] 王新成．多媒体实用技术(图像分册)．电子科技大学出版社，1996
[2] 崔讫．数字图像处理技术与应用．电子工业出版社，1997
[3] 杨长生．图像与声音压缩技术．浙江大学出版社，2000
[4] 沈兰荪．图像编码与异步传输．人民邮电出版社，1998
[5] Kunt M, et al. Second-generation image-coding techniques. Proc IEEE, 19R5, 73(4): 549-574
[6] Barnsley M F, et al. A better way to compress images. Byte, 1988(1): 215-223
[7] Jacquin A. E. Fractal Image Coding: A review. Proc IEEE, 1993, 81(10): 1451-1465
[8] S. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 1989, 11(7): 674-693
[9] Daubechies I. Orthonormal bases of compactly supported wavelets. Comm Pure and Appl Math, 1988, (12): 909-996
[10] Aizawak. Model-based image coding: advanced video coding techniques for very low bit-rate applications. Proc IEEE, 1995, 83(2): 259-271
[11] Antonini M, Barland M, Mathieu P, et al. Image Coding Using Wavelet Transform. IEEE Trans. On Image Processing, 1992, 38(2): 244-250
[12] J.M. Shapiro. Embedded image coding using zerotrees of wavelets coefficients. IEEE Trans. On Signal Processing, Dec. 1993, 41(12): 3445-3462
[13] A. Said, et al. A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits and Systems for Video Tech., June. 1996, 6(3): 243-249
[14] D. wei, et al. Antisymmetric biorthogonal coifiets for image coding. IEEE Trans. On Image Processing, Oct. 1998
[15] Linde Y, et al. An algorithm for vector quantizer design. IEEE Trans Comm, 1980, 28(1): 84-95
[16] 刘贵忠，邸双亮．小波分析及其应用．西安电子科技大学出版社，1992
[17] 程正兴．小波分析算法与应用．西安交通大学出版社，2000．
[18] 沈兰荪．图像编码与异步传输．人民邮电出版社，1998．
[19] 秦前清，杨宗凯．实用小波分析．西安电子科技大学出版社，1998．
[20] 李建平．小波分析与信号处理—理论、应用及软件实现．重庆出版社，2001
[21] 谢鑫，马争鸣．小波分形混合图像编码．中国图像图形学报，2000，5(9)：716-724
[22] Sergio D. Servetto. Image Coding Based on a Morphological Representation of Wavelet Data. IEEE Trans on Image Processing. 1999, 8(9): 1161-1174.
[23] Shen Lansun, Zhang Wenzhong, et al. A wavelet transform based image coding method using vector quantization. In: ICEMI'97: 698-702
[24] Chua L O, Lin T. A Neural Network Approach to Transform Image Coding. Circuit Theory and application, 1988, 16(3): 317-324
[25] Daubechies Ingrid. Ten lectures on wavelet. In: The Society for Industrial and Applied Mathematics, 1992
[26] 胡春玲，陈义宽，马常楼．图像编码时小波基的选择．中国图像图形学报，1998，3(9)：742-745
[27] Heller P, Strang G, Topiwala P et al. The application of multiwavelet filter banks to signal and image processing. IEEE Trans on Image processing, 1998
[28] Francois G. Meyer, Amir Z. Averbuch, and Jan-Olov Stromberg. Fast Adaptive Wavetet Packet Image Compression. Vol. 9, pp. 792-800, May 2000
[29] Z. Xiong, N.P. Galatsanos, M.T. Orchard. Marginal analysis prioritization for image compression based on a hierarchical wavelet decompression. Proc. ICASSP93, 1993: 546-549
[30] Ranaseang V N, Ranganathan N, Namuduri K R. Performance analysis of wavelets in embedded zerotree-based lossless image coding schemes. IEEE Trans on Signal Processing, 1999, 47(3): 884-889
[31] 五祥林，吴国威，林行刚．一种基于零树的多码率小波图像编码方法．电子学报，1997，25(4)：48-51
[32] 杨云峰，苏志勋，钟似玢．一种改进的基于小波零树的图像编码算法．中国图象图形学报，2001，6(6)：542-546
[33] 五嘉，余松熠．一种改进的基于零树集合的小波图像压缩算法．数据采集与处理，2000，15(1)：18-22
[34] 乔瑛．小波变换图像编码的性能评价研究[硕士论文]．西安交通大学．1999
[35] 蔡伟．数字图像的二进制数域小波分解与图像压缩的小波零树嵌入算法[硕士论文]．西安交通大学．1999
[36] 邹采荣，连乃祥，高西奇，何振亚．基于区域联合编码的小波图像压缩方法．数据采集与处理，2000，15(2)：162-165
[37] 周建鹏，杨义先．基于小波分析的静止图像分层编码方法．电子学报，1998，26(1)：20-23
[38] 马维祯，殷瑞祥．子波变换理论及其在信号处理中的应用．数据采集与处理，1994，9(2)：121-129
[39] 薛晓辉，高文．正交小波的零误差边界延拓算法．电子学报，1997，25(11)：11-13
[40] 楼顺天，李博菡．基于Matlab的系统分析与设计(系列)．西安电子科技大学出版社，1998
[41] 何斌，马天予等．Visual C++数字图像处理．人民邮电出版社，2001
[42] A. S. Lewis, G. Knowles. Image Compression Using 2-D Wavelet Transform. IEEE Transactions on Image Processing, VOL. 1, NO. 2, April 1992: 244-250
[43] A. Pentland, B. Horowitz. A practical approach to fractal-based image compression. In Proc. Data Compression Conf., Snowbird. UT, March 1995: 232-241
[44] R. Rinaldo, G. Calvago. Image coding by block prediction of multiresolution subimages. IEEE Trans. on Image Processing, VOL. 4, July 1995: 909-920
[45] Geoffrey M. Dabvis. A Wavelet-Based Analysis of Fraqctal Image Compression. IEEE Trans. on Image Processing, VOL. 7, No. 2, February 1998
[2] 崔讫．数字图像处理技术与应用．电子工业出版社，1997
[3] 杨长生．图像与声音压缩技术．浙江大学出版社，2000
[4] 沈兰荪．图像编码与异步传输．人民邮电出版社，1998
[5] Kunt M, et al. Second-generation image-coding techniques. Proc IEEE, 19R5, 73(4): 549-574
[6] Barnsley M F, et al. A better way to compress images. Byte, 1988(1): 215-223
[7] Jacquin A. E. Fractal Image Coding: A review. Proc IEEE, 1993, 81(10): 1451-1465
[8] S. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 1989, 11(7): 674-693
[9] Daubechies I. Orthonormal bases of compactly supported wavelets. Comm Pure and Appl Math, 1988, (12): 909-996
[10] Aizawak. Model-based image coding: advanced video coding techniques for very low bit-rate applications. Proc IEEE, 1995, 83(2): 259-271
[11] Antonini M, Barland M, Mathieu P, et al. Image Coding Using Wavelet Transform. IEEE Trans. On Image Processing, 1992, 38(2): 244-250
[12] J.M. Shapiro. Embedded image coding using zerotrees of wavelets coefficients. IEEE Trans. On Signal Processing, Dec. 1993, 41(12): 3445-3462
[13] A. Said, et al. A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits and Systems for Video Tech., June. 1996, 6(3): 243-249
[14] D. wei, et al. Antisymmetric biorthogonal coifiets for image coding. IEEE Trans. On Image Processing, Oct. 1998
[15] Linde Y, et al. An algorithm for vector quantizer design. IEEE Trans Comm, 1980, 28(1): 84-95
[16] 刘贵忠，邸双亮．小波分析及其应用．西安电子科技大学出版社，1992
[17] 程正兴．小波分析算法与应用．西安交通大学出版社，2000．
[18] 沈兰荪．图像编码与异步传输．人民邮电出版社，1998．
[19] 秦前清，杨宗凯．实用小波分析．西安电子科技大学出版社，1998．
[20] 李建平．小波分析与信号处理—理论、应用及软件实现．重庆出版社，2001
[21] 谢鑫，马争鸣．小波分形混合图像编码．中国图像图形学报，2000，5(9)：716-724
[22] Sergio D. Servetto. Image Coding Based on a Morphological Representation of Wavelet Data. IEEE Trans on Image Processing. 1999, 8(9): 1161-1174.
[23] Shen Lansun, Zhang Wenzhong, et al. A wavelet transform based image coding method using vector quantization. In: ICEMI'97: 698-702
[24] Chua L O, Lin T. A Neural Network Approach to Transform Image Coding. Circuit Theory and application, 1988, 16(3): 317-324
[25] Daubechies Ingrid. Ten lectures on wavelet. In: The Society for Industrial and Applied Mathematics, 1992
[26] 胡春玲，陈义宽，马常楼．图像编码时小波基的选择．中国图像图形学报，1998，3(9)：742-745
[27] Heller P, Strang G, Topiwala P et al. The application of multiwavelet filter banks to signal and image processing. IEEE Trans on Image processing, 1998
[28] Francois G. Meyer, Amir Z. Averbuch, and Jan-Olov Stromberg. Fast Adaptive Wavetet Packet Image Compression. Vol. 9, pp. 792-800, May 2000
[29] Z. Xiong, N.P. Galatsanos, M.T. Orchard. Marginal analysis prioritization for image compression based on a hierarchical wavelet decompression. Proc. ICASSP93, 1993: 546-549
[30] Ranaseang V N, Ranganathan N, Namuduri K R. Performance analysis of wavelets in embedded zerotree-based lossless image coding schemes. IEEE Trans on Signal Processing, 1999, 47(3): 884-889
[31] 五祥林，吴国威，林行刚．一种基于零树的多码率小波图像编码方法．电子学报，1997，25(4)：48-51
[32] 杨云峰，苏志勋，钟似玢．一种改进的基于小波零树的图像编码算法．中国图象图形学报，2001，6(6)：542-546
[33] 五嘉，余松熠．一种改进的基于零树集合的小波图像压缩算法．数据采集与处理，2000，15(1)：18-22
[34] 乔瑛．小波变换图像编码的性能评价研究[硕士论文]．西安交通大学．1999
[35] 蔡伟．数字图像的二进制数域小波分解与图像压缩的小波零树嵌入算法[硕士论文]．西安交通大学．1999
[36] 邹采荣，连乃祥，高西奇，何振亚．基于区域联合编码的小波图像压缩方法．数据采集与处理，2000，15(2)：162-165
[37] 周建鹏，杨义先．基于小波分析的静止图像分层编码方法．电子学报，1998，26(1)：20-23
[38] 马维祯，殷瑞祥．子波变换理论及其在信号处理中的应用．数据采集与处理，1994，9(2)：121-129
[39] 薛晓辉，高文．正交小波的零误差边界延拓算法．电子学报，1997，25(11)：11-13
[40] 楼顺天，李博菡．基于Matlab的系统分析与设计(系列)．西安电子科技大学出版社，1998
[41] 何斌，马天予等．Visual C++数字图像处理．人民邮电出版社，2001
[42] A. S. Lewis, G. Knowles. Image Compression Using 2-D Wavelet Transform. IEEE Transactions on Image Processing, VOL. 1, NO. 2, April 1992: 244-250
[43] A. Pentland, B. Horowitz. A practical approach to fractal-based image compression. In Proc. Data Compression Conf., Snowbird. UT, March 1995: 232-241
[44] R. Rinaldo, G. Calvago. Image coding by block prediction of multiresolution subimages. IEEE Trans. on Image Processing, VOL. 4, July 1995: 909-920
[45] Geoffrey M. Dabvis. A Wavelet-Based Analysis of Fraqctal Image Compression. IEEE Trans. on Image Processing, VOL. 7, No. 2, February 1998