A speed up technique for the non-local means (NLM) image denoising algorithm based on probabilistic early termination (PET) is proposed. A significant amount of computation in the NLM scheme is dedicated to the distortion calculation between pixel neighborhoods. The proposed PET scheme adopts a probability model to achieve early termination. Specifically, the distortion computation can be terminated and the corresponding contributing pixel can be rejected earlier, if the expected distortion value is too high to be of significance in weighted averaging. Performance comparative with several fast NLM schemes is provided to demonstrate the effectiveness of the proposed algorithm.Index Terms-Early termination, fast algorithm, image denoising, non-local means (NLM) algorithm, probabilistic algorithm.

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This paper presents an efficient view synthesis distortion estimation method for 3-D video. It also introduces the application of this method to Advanced Video Coding (AVC)-and High Efficiency Video Coding (HEVC)-compatible 3-D video coding. Although the proposed view synthesis distortion scheme is generic, its use for actual 3-D video codec systems addresses the many issues caused by different video-coding formats and restrictions. The solutions for these issues are herein proposed. The simulation results show that the proposed scheme can achieve approximately 5.4% and 10.2% coding gains for AVCand HEVC-compatible 3-D coding, respectively. In addition, the results show the remarkable complexity reduction of the scheme compared to the view synthesis optimization method currently used in 3-D-HEVC. The proposed method has been adopted into the presently developing AVC-and HEVC-compatible test model reference software. Index Terms-3-D Advanced Video Coding (AVC), 3-D High Efficiency Video Coding (HEVC), 3-D video (3-DV) codec, depth map coding, multiview coding, view synthesis distortion, view synthesis distortion estimation.

A new technique for film grain noise extraction, modeling and synthesis is proposed and applied to the coding of high definition video in this work. The film grain noise is viewed as a part of artistic presentation by people in the movie industry. On one hand, since the film grain noise can boost the natural appearance of pictures in high definition video, it should be preserved in high-fidelity video processing systems. On the other hand, video coding with film grain noise is expensive. It is desirable to extract film grain noise from the input video as a pre-processing step at the encoder and re-synthesize the film grain noise and add it back to the decoded video as a post-processing step at the decoder. Under this framework, the coding gain of the denoised video is higher while the quality of the final reconstructed video can still be well preserved. Following this idea, we present a method to remove film grain noise from image/video without distorting its original content. Besides, we describe a parametric model containing a small set of parameters to represent the extracted film grain noise. The proposed model generates the film grain noise that is close to the real one in terms of power spectral density and cross-channel spectral correlation. Experimental results are shown to demonstrate the efficiency of the proposed scheme.