Convex Optimization
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Filtering random noise from deterministic signals via datacompression
IEEE Transactions on Signal Processing
Noise reduction by fuzzy image filtering
IEEE Transactions on Fuzzy Systems
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image enhancement and denoising by complex diffusion processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Efficiency analysis of multihypothesis motion-compensated prediction for video coding
IEEE Transactions on Image Processing
Complexity-regularized image denoising
IEEE Transactions on Image Processing
Variational denoising of partly textured images by spatially varying constraints
IEEE Transactions on Image Processing
A new rate control scheme using quadratic rate distortion model
IEEE Transactions on Circuits and Systems for Video Technology
Rate-constrained coder control and comparison of video coding standards
IEEE Transactions on Circuits and Systems for Video Technology
Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising
IEEE Transactions on Circuits and Systems for Video Technology
Video Denoising Based on Inter-frame Statistical Modeling of Wavelet Coefficients
IEEE Transactions on Circuits and Systems for Video Technology
Temporal Video Denoising Based on Multihypothesis Motion Compensation
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, a simultaneous MAP-based video denoising and rate-distortion optimized video encoding algorithm is proposed. We begin with formulating the denoising problem as a maximum a posteriori (MAP) estimate problem. Then, according to the Bayes rule, we show that the MAP estimate is determined by two terms: noise conditional density model and priori conditional density model. Based on the assumptions that the noise satisfies Gaussian distribution and the priori model is measured by the bit-rate, the MAP estimate can be expressed as a rate distortion optimization problem. With this, we are able to simultaneously perform MAP-based video denoising and rate-distortion optimized video encoding under some assumptions. Moreover, we describe in details how to select suitable coding parameters, i.e., quantization parameter, mode, motion vector, reference index, and regularization parameter. Finally, we conduct several experiments to verify our proposed algorithm.