This paper introduces a new hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized free-view. New temporal background modeling is proposed, which incorporates stationary temporal information in the hole-filling process and preserves the temporal consistency of synthesized views. A background codebook is distinguished from a non-overlapped patch-based codebook, which contributes to extracting reliable temporal background information. Furthermore, a depth-map driven spatial local background estimation is also addressed to discriminate the background holes in each disocclusion and to define two spatial BG constraints that represent the lower and upper bounds of a background candidate. Holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. In addition, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view. Finally, an exemplar-based inpainting is applied to fill in the remaining holes with a priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over state-of-the-art methods.

In this paper, we propose a spatially adaptive noise removal algorithm using local statistics that consists of two stages: noise detection and removal. To corporate desirable properties into denoising process, the local weighted mean, local weighted activity, and local maximum are defined. With these local statistics, the noise detection function is defined and a modified Gaussian filter is used to suppress the detected noise components. The experimental results demonstrate the effectiveness of the proposed algorithm.

This paper addresses an adaptive motion vector prediction algorithm to improve the performance of video encoder. The block-based motion vector is characterized by the local statistics so that the coefficients of LS-based linear motion predictor can be optimized. However, it requires very expensive computational cost. The proposed algorithm using LS approach with spatially varying motion-directed property adaptively controls the coefficients of the motion predictor and reduces the computational cost as well as the motion prediction error. Experimental results show the capability of the proposed algorithm.

In this paper, we present an adaptive motion search range decision algorithm for low-power video encoder. The performance, computation, and power consumption of block matching motion estimation algorithms in video coding standards depends on the motion search range. The motion search range to restrict the motion candidates controls the trade-off between the motion accuracy and the encoder complexity. The proposed algorithm adaptively determines the motion search range by local statistics of the neighbor blocks, resulting in dramatic reduction of computational cost of video encoder without the loss of coding efficiency. Experimental results show that the proposed algorithm speeds up encoding time by 1.5-2.8 times, and reduces power consumption to 2.5-7.0 times.

In this paper, an efficient algorithm for simultaneous reduction of blocking and ringing artifacts of compressed video is presented. A pixel based new regularization functional, which consists of four directional regularization functions is to represent the correlation to each direction, is proposed. Also, the regularization parameter, which is critical in regularization restoration problem, is determined from the available overhead information in decoder, such as, COD (Coded Macroblock Indication) and quantization step size, resulting in dramatic computational reduction to determine the regularization parameters. The experimental results show the capability and efficiency of the proposed algorithm.

In this paper, we present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between-channel deterministic information is considered. For each channel a functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters mentioned above are adjusted based on the partially restored image.

This paper introduces an iterative regularized approach to increase the resolution of a video sequence. A multiple input smoothing convex functional is defined and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly interpolated images in the spatial domain, and motion estimation between frames in the temporal domain. An iterative algorithm is utilized for obtaining the solution. The regularization parameter is updated at each iteration step from the partially restored video sequence. Experimental results demonstrate the capability of the proposed approach.

In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.