基于小波算法的视频软解压播放系统的研究方案
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摘要
目前，视频技术的发展日新月异，已在诸多领域有着广泛的应用。然而，由于数字图像的数据量较大，而现在的网络传输速度有限，所以必须针对这一问题对视频数据进行编码、压缩及解码。
小波变换图像编码技术具有与人的视觉特性相结合的潜力，从而可在同样的码率下，获得主观质量更好的重建图像，或者在相同的主观评价条件下，得到更高的图像压缩比。本论文所讨论的就是基于小波变换的视频解压缩系统。
在论文的讨论中，阐述了基于小波变换的视频压缩／解压缩系统的设计方案，详细地讲解了各部分的基本原理及实现。解压缩的核心是ADV611所用的小波核，即二维正交小波变换。从压缩文件输入开始，经过了霍夫曼解码、游程解码、自适应量化的逆变换、小波逆变换。至此，压缩文件已从输入压缩文件变为YUV文件。但是为了播放解压缩后的视频，要将YUV格式转变为RGB格式。最终，用DirectDraw连续播放视频。并将这种基于小波变换的视频压缩／解压缩方案与其它国际通用的视频压缩标准从很多方面进行比较。从而总结出本课题所用的这种基于小波变换的视频压缩／解压缩方案的优、缺点。因此，本课题的主要目的不仅仅是研究开发出一种基于小波变换的视频压缩／解压缩系统，更重要的是证明小波分析理论在视频压缩／解压缩技术领域的巨大潜力。
Nowadays, digital video technology changes with each passing day It is used very popular in many areas. But because of the huge data quantity of digital image, together with the limited transmission speed, we must code, compress and decode the video data.
Wavelet transform encode technology has the potential of fitting with the characteristics of human eyes. So we may get better recovering image under the same transmission speed or better compression ratio under the same evaluation level.
This paper discusses a design plan of video compression/decompression system based on wavelet transform. In it, it was described the basic theory and realization of each part within the system in detail. The core of decompression is wavelet kernel using by ADV611, that is, two-dimensional bi-orthogonal wavelet transform. Starting from compressed file, the video data passed Huffman decoder, Run Length decoder, inverse adaptive quantilizer and inverse transform of wavelet. Then, the compression file has been transformed to YUV format. In order to play the video, we must transform from YUV format to RGB format. At last, we use DirectDraw to play the decoding video. Together, I try to compare this kind of design plan of video compression/decompression system based on wavelet transform with other international video compression standard in many respects. So I made a conclusion of the value and shortcoming of this design. So the purpose of this project is not only research of a video compression/decompression system, but also in order to prove the value and potential of application of wavelet transform in the area of video compression and decompression.
小波变换图像编码技术具有与人的视觉特性相结合的潜力，从而可在同样的码率下，获得主观质量更好的重建图像，或者在相同的主观评价条件下，得到更高的图像压缩比。本论文所讨论的就是基于小波变换的视频解压缩系统。
在论文的讨论中，阐述了基于小波变换的视频压缩／解压缩系统的设计方案，详细地讲解了各部分的基本原理及实现。解压缩的核心是ADV611所用的小波核，即二维正交小波变换。从压缩文件输入开始，经过了霍夫曼解码、游程解码、自适应量化的逆变换、小波逆变换。至此，压缩文件已从输入压缩文件变为YUV文件。但是为了播放解压缩后的视频，要将YUV格式转变为RGB格式。最终，用DirectDraw连续播放视频。并将这种基于小波变换的视频压缩／解压缩方案与其它国际通用的视频压缩标准从很多方面进行比较。从而总结出本课题所用的这种基于小波变换的视频压缩／解压缩方案的优、缺点。因此，本课题的主要目的不仅仅是研究开发出一种基于小波变换的视频压缩／解压缩系统，更重要的是证明小波分析理论在视频压缩／解压缩技术领域的巨大潜力。
Nowadays, digital video technology changes with each passing day It is used very popular in many areas. But because of the huge data quantity of digital image, together with the limited transmission speed, we must code, compress and decode the video data.
Wavelet transform encode technology has the potential of fitting with the characteristics of human eyes. So we may get better recovering image under the same transmission speed or better compression ratio under the same evaluation level.
This paper discusses a design plan of video compression/decompression system based on wavelet transform. In it, it was described the basic theory and realization of each part within the system in detail. The core of decompression is wavelet kernel using by ADV611, that is, two-dimensional bi-orthogonal wavelet transform. Starting from compressed file, the video data passed Huffman decoder, Run Length decoder, inverse adaptive quantilizer and inverse transform of wavelet. Then, the compression file has been transformed to YUV format. In order to play the video, we must transform from YUV format to RGB format. At last, we use DirectDraw to play the decoding video. Together, I try to compare this kind of design plan of video compression/decompression system based on wavelet transform with other international video compression standard in many respects. So I made a conclusion of the value and shortcoming of this design. So the purpose of this project is not only research of a video compression/decompression system, but also in order to prove the value and potential of application of wavelet transform in the area of video compression and decompression.
引文
[1] Ebrahimi T., Li W. New trends in very low bitrate video coding. Proc. IEEE,1995, 83:877-891
[2] Kunt M., Ikonomopoulos A., Kocher M. Second generation image coding techniques. Proc. IEEE Trans. Commun. Tech, 1985, 73:549-575
[3] Habibi A. Comparison of the nth-order DPCM encoder with linear transformations and block quantization techniques. IEEE Trans. Commun. Tech.,1971, 19:948-956
[4] Ahmed N., Natarajan T., Rao K. R. Discrete cosine transform. IEEE Trans. Comput., 1974, C-23:90-93页
[5] 崔锦泰，程正兴译。小波分析导论。西安交通大学出版社，1995
[6] Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. IT. 1990, 36(5)
[7] Mallat S.Multiresolution approximation and wavelets. Trans. Am. Math. Soc.,1989, 135:69-88页
[8] Mallat S.A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. PAMI., 1989, 3:674-693
[9] Daubechies I.Orthonorma Basis of compactly supported wavelets. Comm.Pure Applied Math., 1988, 41:909-996页
[10] Mallat S.Multifrequency channel decomposition of image and wavelet model. IEEE Tran. ASSR, 1987, 37(12): 2091-2110
[11] 焦李成，保铮．子波理论与应用：进展与展望．电子学报，1993，21(7):91-96
[12] Zhang Y.Q., Zafar S. Motion-compensated wavelet transform coding for video compression. IEEE Trans. Circuit Syst. Video Technol., 1992, 2:285-295
[13] Martucci S. A., etc. A zerotree wavelet video coder. IEEE Trans. Circuit Syst.Video Technol., 1997, 7(1)
[14] Kenneth R. Castleman, Digital Image Processing (数字图像处理), Prentice Hall,1996
[15] 陈逢时，子波变换理论及其在信号处理中的应用，国防工业出版社，1998
[16] 程正兴，小波分析算法与应用，西安交通大学出版社，1998
[17] 秦前清等，实用小波分析，西安电子科技大学出版社，1995
[18] 郑南宁，计算机视觉与模式识别，国防工业出版社，1998
[19] 高文．多媒体数据压缩技术．北京：电子工业出版社．1994
[20] 余松煜．现代图像信息压缩技术．北京：科学出版社．1998
[21] 沈兰荪．图像编码与异步传输．北京：人民邮电出版社．1998
[22] 吴乐南．数据压缩的原理与应用．北京：电子工业出版社．1995
[23] 黄贤武等．数字图像处理与压缩编码技术．电子科技大学出版社．2000
[24] 精英科技编著．视频压缩与音频编码技术．中国电力出版社．2001
[25] Daubechies I. Orthonormal bases of compactly supported wavelets. Comm.Pure Appl. Math., 1988, 41: 909～996
[26] Daubechies I. Orthonormal bases of compactly supported wavelets Ⅱ,Variations on a theme. SIAM. J. Math. Anal., 1993, 24:499～519
[27] Mallat S G. A theory of multiresolution signal decomposition:the wavelet representation. IEEE Trans,Pattern Anal. and Machine Intell., 1989, PAMI-11,674～693
[28] Coifman R R, Wickenhauser M V. Entropy-based algorithms for best basis selection. IEEE Trans. Infor. Theory, 1992, 38:713～718
[29] Lewis A S,Knowles G. Image compression using the2 -D wavelet transform. IEEE Trans. on Image Processing, 1992, 1:244～250
[30] Ramchandran K, Vetterli M. Best wavelet packet bases in a rate-distortion sense. IEEE Trans. Image Processing, 1992, 2:160～175页
[31] Autonini M, Rarland M, Mathiou P, Daubechies I. Image coding using wavelet transform. IEEE Trans. on Image Processing, 1992, 1:205～220
[32] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. on Signal Proc., 1993, 41:3445～3462
[2] Kunt M., Ikonomopoulos A., Kocher M. Second generation image coding techniques. Proc. IEEE Trans. Commun. Tech, 1985, 73:549-575
[3] Habibi A. Comparison of the nth-order DPCM encoder with linear transformations and block quantization techniques. IEEE Trans. Commun. Tech.,1971, 19:948-956
[4] Ahmed N., Natarajan T., Rao K. R. Discrete cosine transform. IEEE Trans. Comput., 1974, C-23:90-93页
[5] 崔锦泰，程正兴译。小波分析导论。西安交通大学出版社，1995
[6] Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. IT. 1990, 36(5)
[7] Mallat S.Multiresolution approximation and wavelets. Trans. Am. Math. Soc.,1989, 135:69-88页
[8] Mallat S.A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. PAMI., 1989, 3:674-693
[9] Daubechies I.Orthonorma Basis of compactly supported wavelets. Comm.Pure Applied Math., 1988, 41:909-996页
[10] Mallat S.Multifrequency channel decomposition of image and wavelet model. IEEE Tran. ASSR, 1987, 37(12): 2091-2110
[11] 焦李成，保铮．子波理论与应用：进展与展望．电子学报，1993，21(7):91-96
[12] Zhang Y.Q., Zafar S. Motion-compensated wavelet transform coding for video compression. IEEE Trans. Circuit Syst. Video Technol., 1992, 2:285-295
[13] Martucci S. A., etc. A zerotree wavelet video coder. IEEE Trans. Circuit Syst.Video Technol., 1997, 7(1)
[14] Kenneth R. Castleman, Digital Image Processing (数字图像处理), Prentice Hall,1996
[15] 陈逢时，子波变换理论及其在信号处理中的应用，国防工业出版社，1998
[16] 程正兴，小波分析算法与应用，西安交通大学出版社，1998
[17] 秦前清等，实用小波分析，西安电子科技大学出版社，1995
[18] 郑南宁，计算机视觉与模式识别，国防工业出版社，1998
[19] 高文．多媒体数据压缩技术．北京：电子工业出版社．1994
[20] 余松煜．现代图像信息压缩技术．北京：科学出版社．1998
[21] 沈兰荪．图像编码与异步传输．北京：人民邮电出版社．1998
[22] 吴乐南．数据压缩的原理与应用．北京：电子工业出版社．1995
[23] 黄贤武等．数字图像处理与压缩编码技术．电子科技大学出版社．2000
[24] 精英科技编著．视频压缩与音频编码技术．中国电力出版社．2001
[25] Daubechies I. Orthonormal bases of compactly supported wavelets. Comm.Pure Appl. Math., 1988, 41: 909～996
[26] Daubechies I. Orthonormal bases of compactly supported wavelets Ⅱ,Variations on a theme. SIAM. J. Math. Anal., 1993, 24:499～519
[27] Mallat S G. A theory of multiresolution signal decomposition:the wavelet representation. IEEE Trans,Pattern Anal. and Machine Intell., 1989, PAMI-11,674～693
[28] Coifman R R, Wickenhauser M V. Entropy-based algorithms for best basis selection. IEEE Trans. Infor. Theory, 1992, 38:713～718
[29] Lewis A S,Knowles G. Image compression using the2 -D wavelet transform. IEEE Trans. on Image Processing, 1992, 1:244～250
[30] Ramchandran K, Vetterli M. Best wavelet packet bases in a rate-distortion sense. IEEE Trans. Image Processing, 1992, 2:160～175页
[31] Autonini M, Rarland M, Mathiou P, Daubechies I. Image coding using wavelet transform. IEEE Trans. on Image Processing, 1992, 1:205～220
[32] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. on Signal Proc., 1993, 41:3445～3462
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