基于噪声分布特性的压缩视频超分辨率重建

徐忠强; 朱秀昌

doi:10.3724/SP.J.1146.2006.01255

基于噪声分布特性的压缩视频超分辨率重建

doi: 10.3724/SP.J.1146.2006.01255

基金项目:

江苏省自然科学基金(BK2004151)资助课题

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出版历程
- 收稿日期: 2006-08-23
- 修回日期: 2007-05-17
- 刊出日期: 2008-03-19

Super-Resolution Reconstruction of Compressed Video Based on Noise Distribution Property

摘要

摘要: 该文通过对视频压缩过程进行建模，利用比特流中的量化信息和运动信息，建立了量化噪声和运动估计噪声模型，并考虑成像过程的加性噪声，总的噪声模型对不同的量化器可自适应调整。以Huber-Markov 随机场作为图像的先验模型，用梯度下降法进行MAP超分辨率重建，对其特性进行了分析。仿真实验表明，该算法重建图像的主、客观质量较高。
- 压缩视频; 超分辨率重; MAP; DCT量化噪声; 运动估计噪声
Abstract: This paper models the process of video compression, DCT quantization noise and motion estimation noise with exploiting the quantization step size and motion information embedded in the bit-stream. Together with the additive noise term of imaging, the proposed total noise term adaptively adjusts for different quantizers. With a Huber-Markov Random Field (HMRF) as the prior model, the gradient descent algorithm and MAP super-resolution reconstruction are presented and their performances are also analyzed. Simulation results show that proposed algorithm obtains better objective and subjective performances.

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参考文献(1)

Tsai R and Huang T. Multi-frame image restoration and registration. Advances in Computer Vision and Image Processing, 1984, (1): 317-339.[2]Stark H and Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections[J].Journal of the Opt. Soc. of America (A.1989, 6 (11):1715-1726[3]Patti A J, Sezan M I, and Tekalp A M. Super-resolution video reconstruction with arbitrary sampling lattices and nonzero aperture time[J].IEEE Trans. on Image Processing.1997, 6(8):1064-1076[4]Schultz R R and Stevenson R L. Extraction of high resolution frames from video sequences[J].IEEE Trans. on Image Processing.1996, 5 (6):996-1001[5]Hardie R C, Barnard K J, and Armstrong E E. Joint MAP registration and high-resolution image estimation using a sequence of under-sampled images[J].IEEE Trans. on Image Processing.1997, 6(12):1621-1633[6]Gunturk B K, Altunbasak Y, and Mersereau R M. Super-resolution reconstruction of compressed video using transform-domain statistics[J].IEEE Trans. on Image Processing.2004, 13(1):33-43[7]Altunbasak Y, Patti A J, and Mersereau R M. Super-resolution still and video reconstruction from MPEG coded video[J].IEEE Trans. on Circuits and System for Video Technology.2002,12(4):217-226[8]Segall C A, Molina R, and Katsaggelos A K. High-resolution images from low-resolution compressed video[J].IEEE Signal Processing Magazine.2003, 20(3):37-48[9]Segall C A, Katsaggelos A K, Molina R, and Mateos J. Bayesian resolution enhancement of compressed video[J].IEEE Trans. on Image Processing.2004, 13(7):898-911[10]Robertson M A and Stevenson R L. Temporal resolution enhancement in compressed video sequences. EURASIP Journal on Applied Signal Processing, 2001, 4: 230-238.[11]Schultz R R, Li Meng, and Stevenson R L. Subpixel motion estimation for super-resolution image sequence enhancement[J].Journal of Visual Communications and Image Representation.1998, 9(1):38-50[12]ORourke T P and Stevenson R L. Improved image decompression for reduced transform coding artifacts[J].IEEE Trans. on Circuits and Systems for Video Technology.1995, 5(6):490-499[13]Robertson M A and Stevenson R L. DCT quantization noise in compressed images[J].IEEE Trans. on Circuits and Systems for Video Technology.2005, 15(1):27-38[14]Schultz R R and Stevenson R L. A Bayesian approach to image expansion for improved definition[J].IEEE Trans. on Image Processing.1994, 3(2):233-242

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