Please use this identifier to cite or link to this item:
A Study of Improving the Quality of VQ Decompressed Image Based on DWT
discrete wavelet transform
|引用:|| R. M. Gray, “Vector Quantization,” IEEE ASSP Magazine, vol. 1, no. 2, pp. 4-29, Apr. 1984.  McAndrew, “Introduction to Digital Image Processing with MATLAB”, 2005.  G. E. Tsekouras, “A Fuzzy Vector Quantization Approach to Image Compression,” Applied Mathematics and Computation, vol. 167, no. 1, pp. 539-560, Aug. 2005.  W. Xu, A. K. Nandi and J. Zhang, “Novel fuzzy reinforced learning vector quantisation algorithm and its application in image compression,” IEE Proceedings Vision, Image, and Signal Processing, vol. 150, no. 5, Oct. 2003.  R. Lancini and S. Tubaro, “Adaptive vector quantization for picture coding using neural networks,” IEEE Transactions on Communications, vol. 43, no. 234, pp. 534-544, Apr. 1995.  J. H. Jeng, K. D. Wu and Y. L. Lin, “PCA-ANN for Image Coding”, I-Shou University, Thesis.  P. U. Chavan and P. P. Chavan, “Codebook Optimization in Vector Quantization using Genetic Algorithm,” Second International Conference on Computer and Electrial Engineering, vol. 1, pp. 280-283, 2009.  L. Ying, Z. Hui and W. F. Yu, “Image Vector Quantization Coding Based on Genetic Algorithm,” IEEE International Conference on Robotic, Intelligent Systems and Signal Processing, vol. 2, pp. 773-777, 2003.  C. L. Kuo and H. M. Sum, “Fast Partial Codebook Search Algorithm for Vector Quantization,” Thesis, National Cheng Kung University, 2003.  S. Chen, Z. He, and B. L. Luk, “A generic postprocessing technique for image compression,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 4, pp. 546 - 553, Apr. 2001.  J. J. Shen and H. C. Huang, “An Adaptive Image Compression Method Based on Vector Quantization,” First International Conference on Pervasive Computing Signal Processing and Applications, pp. 377-381, 2010.  K. L. Hung, C. C. Chang and T. S Chen, “Reconstruction of Lost Blocks Using Codeword Estimation,” IEEE Transactions on Consumer Electronics, vol. 45, no. 4, pp. 1190-1199, 1999..  T. H. Chen and C. S. Wu, “Compression-unimpaired batch-image encryption combining vector quantization and index compression,” Information Sciences, vol.180, no.9, pp. 1690-1701, May. 2010.  Z. H. Wang, C. C. Chang, K. N. Chen and M. C. Li, “An Encoding Method for Both Image Compression and Data Lossless Information Hiding,” The Journal of Systems and Software, vol. 83, no. 11, pp. 2073-2082, Nov. 2010.  Y. S. Tsai and P. Tsai, “Adaptive Data Hiding for Vector Quantization Images Based on Overlapping Codeword Clustering, ” Information Science, vol. 181, no. 15, Mar. 2011.  Y. G. Wu and C. H. Wu, “Image ”Image Vector Quantization Codec Indices Recovery Using Lagrange Interpolation,“ Image and Vision Computing, vol. 26, no. 8, pp. 1171-1177, Aug. 2008.  陳同孝，張真誠，黃國峰, “數位影像處理技術”,2001.  C. Christopoulos, A. Skodras and T. Ebrahmi, “The JPEG2000 Still Image Coding System: An Overview,” IEEE Transactions on Communication Electronics, vol. 4, pp. 1103-1127, 2000.  W. Sweldens, “The Lifting Scheme: A Custom-Design Construction of Biorthogonal Wavelet”, Applied and Computational Harmonic Analysis, vol.3, pp. 186-200, 1996.  吳炳飛，胡益強，瞿忠正，蘇崇彥，林重甫，”JPEG2000 影像壓縮技術” , 2003.  M. Shabanifard and M. G. Shayesteh, “A New Compression Method Based on LBG Algorithm in DCT Domain,” Seventh Iranian Machine Vision and Image Processing, pp. 1-5, 2011.  T. Phanprasit, T. Leauhatong, C. Pintavirooj and M. Sangworasil, “Image Coding Using Vector Quantization Based on Wavelet Transform Fuzzy C-Means and Principle Component Analysis,” International Symposium on Communication and Information Technology, pp. 448-453, 2009.  Q. Huang, Y. Wang and S. Chang, “High-performance FPGA Implementation of Discrete Wavelet Transform for Image Processing,” Symposium on Photonics and Optoelectronics, pp. 1-4, 2011.  S. F. Lin, H. C. Hsin and C. K. Su, “Hybrid Image Compression Based on Set-Partitioning Embedded Block Coder and Residual Vector Quantization,” Journal of Information Science and Engineering, pp. 1011-1027, 2010.  S. Lin and E. Salari, “Image Coding Using wavelet transform and classified Vector quantization,” IEEE Proceedings Vision, Image and Signal Processing, vol.143, no. 5, pp.285-291, 1996.  K. Bao and X. G. Xia, “Image Compression using a new discrete multiwavelet transform and a new embedded vector quantization,” IEEE Transcations Circits and Systems for Video Technology, vol. 10, no.6, pp.833-842, 2000.  G. Strang and T. Nguyen, “Wavelets and Filter Banks,” 1996.  S. E. Umbaugh, “Computer Vision and Image Processing: A Practical Approach Using CVIPTools,” Prentice Hall, 1998.  J. Shukla, M. Alwani and A.K.Tiwari, “A Survey on Lossless Image Compression Methods,” 2nd International Conference on Computer Engineering and Technology, vol. 6, pp.136-141, 2010.  H. Matsumoto, K. Sasazaki and Y. Suzuki, “Color Image Compression with Vector Quantization,” IEEE Conference on Soft Computing in Industrial Applications, pp. 84-88, 2008.  P. Vprek, and A. B. Bradley, “An Improved Algorithm for Vector Quantizer Design,” IEEE Signal Processing Letters, vol. 7, no. 9, pp. 250-252, Sep. 2000.  N. M. Nasrabadi and R. A. King, “Image Coding Using Vector Quantization: A Review,” IEEE Transactions on Communication, vol. 36, no. 8, 1988.  K. R. Rao and P. Yip, “Discrete Cosine Transform: Algorithms, Advantages, Applications,” 1990.  W. A. Pearlman, A. Islam, N. Nagaraj and A. Said, “Efficient, low-complexity image coding with a set-partitioning embedded block coder,” IEEE Transactions on Circuits Systems for Video Technology, Vol. 14, pp. 1219-1235, 2004.  M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies, “Image coding using wavelet transform” IEEE Transactions on Image Processing, vol. 1, no. 2, pp. 205-220, Apr. 1992.|
|摘要:||隨著科技的發達，越來越多的影像在網際網路上被流傳，因此有不少的壓縮技術被提出來處理這些影像。以傳統VQ (Vector Quantization)的方式對影像進行壓縮編碼，可以得到不錯的壓縮比，影像的還原品質也在人眼可接收的範圍內，但其壓縮後還原的影像品質無法有效提升。所以如何兼顧壓縮比和影像的還原品質，是一個重要的議題。此篇論文中，以VQ技術為基礎提出兩種影像壓縮方法。在方法一中，我們利用原始影像的區塊平均值將原始影像轉化成差值矩陣(Difference matrix)，然後用若干影像之差值矩陣訓練一本差值編碼簿藉此來壓縮影像。此方法雖然可以利用更小的索引表還原出更好的影像品質，還得要額記住每個區塊像素值的平均數。但也由於影像特性，此一額外資訊可以壓縮為較小的資料檔。
第一個方法雖然得到好的影像品質，但也因為額外資訊所以降低了影像壓縮率。在第二個方法中，我們也得到了一本索引表和一個差值矩陣。主要先將原始影像進行DWT (discrete wavelet transform)轉換，然後以VQ方法壓縮DWT後的轉換影像，達到影像壓縮目的。藉由縮小索引表的總位元數，一方面也調整了差值矩陣的差值來改善影像品質和壓縮率。此外，並紀錄VQ解壓縮後之DWT轉換影像與原始DWT轉換影像的差值矩陣，做為解壓縮後影像品質的調整依據。藉由差值矩陣的誤差控制，可以將VQ壓縮法由失真壓縮調整到近乎無失真壓縮。實驗結果皆顯示，藉由索引表和差值矩陣的可調整性，影像品質和壓縮率得到明顯的改善。|
Image distribution on the Internet is becoming frequent and common with the development of digital technology. So, there are many compression techniques to be proposed to process these images. The better compression rate can be achieved by traditional vector quantization (VQ) method, and the quality of the recovered image can also be accepted. But, the decompressed image quality can’t be promoted efficiently, so how to balance the image compression rate and the image recovering quality is an important issue. In this thesis, we propose two methods based on VQ scheme. Firstly, a new approach of VQ encoding is proposed to compress the image, we transform each image into a difference value’s matrix rather than a pixel value’s image before VQ compression. On the other words, we changed the subject of VQ scheme from an image to its corresponding difference value’s matrix. In this method, the better decompressed image quality can be achieved by a smaller index table, but some additional recovery data of pixel value from its difference value, should be recorded. However, according to the image characteristics, the recovery data can be compressed into a group of smaller data. Although the better image quality can be achieved by the first method, the image compression rate is decreased due to the additional information. By our second method, an index table and a difference matrix are also generated. In order to improve the image quality and image compression rate, any giving image is transformed by discrete wavelet transform (DWT) to generate its DWT image which will be compressed by VQ method further. The total number of bits used in the DWT image’s index table will be reduced, and the difference values of difference matrix will be adjusted. Besides, we compute the difference values between the original DWT image and the VQ decompressed DWT image to get their difference matrix which is the adjustable basis of the final decompressed image quality. By controlling the deviation of this difference matrix, the case of nearly lossless compression for VQ method can be achieved. Experimental results show that our method can improve the image quality and compression rate efficiently by properly tuning the deviation of difference matrix and the size of VQ codebook.
|Appears in Collections:||資訊管理學系|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.