This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. Consequently, the local edge directions are represented by length-16 vectors, which are denoted as weight vectors. The weight vectors are used to formulate geometric regularity constraint, which is imposed on the interpolated image through the Markov Random Field (MRF) model. Furthermore, the interpolation problem is formulated as a Maximum A Posterior (MAP)-MRF problem and, under the MAP-MRF framework, the desired interpolated image corresponds to the minimal energy state of a two-dimensional random held. Simulated Annealing method is used to search for the minimal energy state from a reasonable large state space. Simulation and comparison results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity.

In this paper, a motion-compensated de-interlacing algorithm using the Markov random field (MRF) model is proposed. The de-interlacing problem is formulated as a maximum a posteriori (MAP) MRF problem. The MAP solution is the one that minimizes an energy function, which imposes discontinuity-adaptive smoothness (DAS) spatial constraint on the de-interlaced frame. The edge direction information, which is used to formulate the DAS constraint, is implicitly indicated by weight vectors (weights for 16 digitized directions). Generally, large weights are assigned to along-edge directions and relatively small weights are assigned to across-edge directions. As a local statistical-based method, the proposed weighting method should be more robust than traditional edge-directed interpolation methods in deciding local edge directions. The proposed algorithm is implemented by an iterative optimization process, which guarantees convergence. However, a global optimal solution is not guaranteed due to computational complexity concern. Simulation results compare the proposed algorithm to other motion compensated de-interlacing algorithms. Significant improvements of de-interlaced edges are observed.

In this paper, a de-interlacing algorithm to find the optimal deinterlaced results given accuracy-limited motion information is proposed. The de-interlacing process is formulated as a maximum a posteriori (MAP)-Markov random field (MRF) problem. The MAP solution is the one that minimizes an energy function. The energy function imposes discontinuity adaptive smoothness constraint upon the deinterlaced frame. Simulation results show that the MAP-MRF formulation is efficient and the high frequency noise is removed in a few iterations.

The design of a class of wavelet filters and its application in scalable video coding (SVC) is discussed in detail in this paper. The design method uses maximal flat wavelet filters as prototype filters and incorporates all other wavelet filter design requirements. The designed wavelet filters are optimal in a sense that best tradeoff between high stopband attenuation of analysis lowpass filter H/sub 0/(z) and flat passband response of synthesis lowpass filter F/sub 0/(z) is achieved. The simulation that compares the performances of the designed filters and Daubechies (9,7) filters in SVC are illustrated.

A DCT-based phase correlation motion estimation algorithm is proposed in this paper. By combining four real transforms, a new complex linear phase transform is obtained and is used in phase correlation motion estimation. The application in video compression is discussed in detail. The simulation results show that the proposed algorithm is robust and efficient.