In this letter, we propose a spatially adaptive image restoration algorithm, using local statistics. The local variance, mean and maximum value are utilized to constrain the solution space. These parameters are computed at each iteration step using partially restored image. A parameter defined by the user determines the degree of local smoothness imposed on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared with the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained. Experimental results demonstrate the capability of the proposed algorithm.

In this letter, we propose a spatially adaptive noise removal algorithm using local statistics. The proposed algorithm consists of two stages: noise detection and removal. In order to solve the trade-off between the effective noise suppression and the over-smoothness of the reconstructed image, local statistics such as local maximum and the local weighted activity is defined. With the 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.

In this paper, we propose new low-hardware-cost motion estimation with a large search range for VLSI multimedia processors. It reduces the hardware amount required for pixel comparison by reducing both the spatial-resolution and bit-resolution of pixel values. Low-hardware-cost block-matching criterion is also employed. To avoid performance degradation from low resolution, we introduce an "outlier" pixel with large overload quantization error in the search window, and a search position is excluded from the motion estimation if its corresponding search window block contains one or more outliers. The proposed motion estimation is easy to implement in VLSI multimedia processors, and it significantly reduces the hardware amount when the search range is larger than 6464. In MPEG2 MP@ML video compression with 128128 search range, it reduces the hardware cost to 1/144 that of the full search algorithm, while its degradation of peak signal-to-noise ratio is 0.32 dB.

In this paper, we propose a spatially adaptive gradient-projection algorithm for the H.264 video coding standard to remove coding artifacts using local statistics. A hybrid method combining a new weighted constrained least squares (WCLS) approach and the projection onto convex sets (POCS) approach is introduced, where weighting components are determined on the basis of the human visual system (HVS) and projection set is defined by the difference between adjacent pixels and the quantization index (QI). A new visual function is defined to determine the weighting matrices controlling the degree of global smoothness, and a projection set is used to obtain a solution satisfying local smoothing constraints, so that the coding artifacts such as blocking and ringing artifacts can be simultaneously removed. The experimental results show the capability and efficiency of the proposed algorithm.

We present a simple bit allocation scheme based on adaptive coding for MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing) systems with V-BLAST (Vertical-Bell laboratories LAyered Space-Time) detector. The proposed scheme controls the code rate of the channel coding and assigns the same modulation and coding to the set of selected sub-channels, which greatly reduces the feedback burden while achieving good performance. Simulation results show that the proposed scheme with minimal feedback provides significant performance improvement over other systems.

This paper introduces an adaptive low complexity pre-processing filter to improve the coding performance of seriously degraded video sequences that is caused by the additive noise. The additive noise leads to a decrease in coding performance due to the high frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

This paper introduces a fast image denoising algorithm by estimating noise parameters without prior information about the noise. Under the assumption that additive noise has a Gaussian distribution, the noise parameters were estimated from an observed degraded image, and were used to define the constraints of a noise detection process that was coupled with a Markov random field (MRF). In addition, an adaptive modified weighted Gaussian filter with variable window sizes defined by the constraints on noise detection was used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

This paper presents a new hole-filling method that uses extrapolated spatio-temporal background information to obtain a synthesized free-view. A new background codebook for extracting reliable temporal background information is introduced. In addition, the paper addresses estimating spatial local background to distinguish background and foreground regions so that spatial background information can be extrapolated. Background holes are filled by combining spatial and temporal background information. Finally, exemplar-based inpainting is applied to fill in the remaining holes using a new priority function. The experimental results demonstrated that satisfactory synthesized views can be obtained using the proposed algorithm.

This letter introduces a non-local means (NLM) denoising algorithm that uses a weight function based on a switching norm. The noise level and local activity are incorporated into the NLM denoising algorithm which enhances performance. This is done by selecting a norm among l1, l2, and l4 norms to determine a weighting function. The experimental results show the capability of the proposed algorithm. In addition, the proposed algorithm is verified as effective for enhancing the performance of other NLM algorithms.

In this paper, we introduce a low computing post processing algorithm to simultaneously suppress blocking and ringing artifacts of compressed video sequences. A new regularization function to incorporate smoothness to neighboring pixels is defined, where the function is composed of four sub-functions combined with pixel-based data fidelity and smoothing terms. Therefore, the solution can be obtained without inverse matrix or vector-matrix computation, so that low complexity implementation is possible. In addition, the regularization parameter controlling the relative importance between the data fidelity and the degree of smoothness is estimated from the available overhead information in decoder, such as, macroblock type and quantization step size. The experimental results show the capability and efficiency of the proposed algorithm.