基于局部DCT系数的图像压缩感知编码与重构

潘榕; 刘昱; 侯正信; 汪少初

doi:10.3724/SP.J.1004.2011.00674

基于局部DCT系数的图像压缩感知编码与重构

doi: 10.3724/SP.J.1004.2011.00674

1.
天津大学电子信息工程学院天津 300072

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出版历程
- 收稿日期: 2010-06-28
- 修回日期: 2011-02-28
- 刊出日期: 2011-06-20

Image Coding and Reconstruction via Compressed Sensing Based on Partial DCT Coefficients

1.
School of Electronic Information Engineering, Tianjin University, Tianjin 300072

摘要

摘要: 引入了压缩感知(Compressed sensing, CS)理论, 给出了在获取局部二维离散余弦变换(Discrete cosine transform, DCT)系数的基础上高质量地编码与重构图像的新方法. 研究了在无量化和有量化情况下, 基于局部DCT系数的图像CS最小全变差重构算法. 在对DCT系数进行量化的过程中得到含噪的局部DCT系数, 在此基础上设计了能完成CS重构的图像编解码一般流程, 并构建了实际应用系统. 实验结果表明, 对于稀疏性较强的图像, 在图像编解码系统中结合CS理论与方法能得到高质量的重构图像, 与传统的直接反离散余弦变换(Inverse DCT, IDCT)方法相比, 峰值信噪比(Peak signal to noise ratio, PSNR)最大能提高5dB以上, 对于一般图像, PSNR也有较大提高.
- 图像编码 /
- 图像重构 /
- 离散余弦变换 /
- 压缩感知 /
- 最小全变差
Abstract: This paper introduced the CS (compressed sensing) theory and proposed a new method to encode and reconstruct images after acquiring the partial two-dimensional DCT (discrete cosine transform) coefficients. The CS total variation reconstruction algorithms based on partial DCT coefficients with or without quantization were studied in this paper. In the quantization step, partial noisy DCT coefficients were obtained, based on which the general image coding/decoding process able to realize the CS reconstruction was designed, and a practical application system was made up. The experimental results showed that for images with strong sparsity, the image coding/decoding system integrated with CS theory and its methods can be used to obtain reconstructed images with high quality, and that compared with traditional direct inverse DCT (IDCT) method, the improved peak signal to noise ratio (PSNR) can be up to 5dB, and it also has some improvement in the term of PSNR for general images.
- Image coding /
- image reconstruction /
- discrete cosine transform (DCT) /
- compressed sensing (CS) /
- minimum total variation (TV)

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[1]

Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306 [2] Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509 [3] Li Shu-Tao, Wei Dan. A survey on compressive sensing. Acta Automatica Sinica, 2009, 35(11): 1369-1377(李树涛, 魏丹. 压缩传感综述. 自动化学报, 2009, 35(11): 1369-1377)[4] Shi Guang-Ming, Liu Dan-Hua, Gao Da-Hua, Liu Zhe, Lin Jie, Wang Liang-Jun. Advances in theory and application of compressed sensing. Acta Electronica Sinica, 2009, 37(5): 1070-1081(石光明, 刘丹华, 高大化, 刘哲, 林杰, 王良君. 压缩感知理论及其研究进展, 电子学报, 2009, 37(5): 1070-1081)[5] Yang J F, Zhang Y, Yin W T. A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 288-297 [6] Yao Qing-Dong, Bi Hou-Jie, Wang Zhao-Hua, Xu Meng-Xia. Image Coding Primer (Third Edition). Beijing: Tsinghua University Press, 2006. 50(姚庆东, 毕厚杰, 王兆华, 徐孟侠. 图像编码基础(第三版). 北京: 清华大学出版社, 2006. 50)[7] Xie X C, Yu L J. A new video codec based on compressed sensing. In: Proceedings of the 2nd International Congress on Image and Signal Processing. Tianjin, China: IEEE, 2009. 1-5[8] Sarkis M, Diepold K. Depth map compression via compressed sensing. In: Proceedings of the 16th IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, 2009. 737-740[9] Zhang Y F, Mei S L, Chen Q Q, Chen Z B. A novel image/video coding method based on compressed sensing theory. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas, USA: IEEE, 2008. 1361-1364[10] Candes E J, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems, 2007, 23(3): 969-985[11] Candes E J, Tao T. Decoding by linear programming. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215[12] Boyd S, Vandenberghe L. Convex Optimization. Cambridge: Cambridge University Press, 2004[13] Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 21-30[14] Bjontegaard G. Calculation of Average PSNR Differences between RD-curves, Technical Report VCEG-M33, the 13th Meeting of Video Coding Experts Group-SG16, USA, 2001

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