Young Duk Kim

Adaptive Arbitration of Intra-Field and Motion Compensation Methods for De-Interlacing

De-interlacing based on motion compensation (MC) is one of the best ways of improving the resolution of a progressive video converted from an interlaced source. However, the converted frames often suffer from serious defects like feathering artifacts in regions with inaccurate motion vectors (MVs). In such regions, an intra-field method that is robust to MV errors can be used to correct motion compensation artifacts (MCAs). In this letter, we propose an adaptive arbitration method to combine intra-field and MC methods adequately. The proposed method considers the reliability of MC results along with the MV reliability measured by the spatio-temporal consistency of MVs and displaced pixel differences. The MC reliability is determined by detecting MCAs in MC results, and then the MV reliability is adjusted according to the MC reliability. Also, adaptive-weight MC and pseudo MC methods are proposed to provide more reliable MC results and to improve the accuracy of MCA detection, respectively. Experimental results show that the proposed method provides high-quality video sequences while reducing many visible artifacts.

Interlaced-to-progressive conversion using adaptive projection-based global and representative local motion estimation

We propose a motion-compensation-based deinterlacing nalgorithm using global and representative local motion estimation. nThe proposed algorithm first divides an entire image into five regions nof interest (ROIs) according to the temporally predicted motion type n(i.e., global or local) and the spatial position. One of them is for nglobal motion estimation and the others are for local motion estimation. nThen, dominant motions of respective ROIs are found by adaptive nprojection approach. The adaptive projection method not only nestimates dominant local motions with low computational cost, but nalso ensures consistent global motion estimation. Using the estimated nmotion vectors, adaptive two-field bidirectional motion compensation nis performed. The arbitration rules, measuring the reliability nof motion compensation accurately, produce high-quality ndeinterlaced frames by effectively combining the results of motion ncompensation and the stable intrafield deinterlacing. Experimental nresults show that the proposed deinterlacing algorithm provides better nimage quality than the existing algorithms in both subjective and nobjective measures.

Global motion-compensated preprocessing algorithm for block-based frame-rate conversion

We propose a global motion (GM) compensated preprocessing algorithm for block-based frame rate conversion (FRC). The proposed method estimates camera motions such as zooms or rotations between two input frames, and accordingly, produces nontranslational GM-free pictures by performing GM compensation with respect to the temporal location where the FRC method reconstructs an intermediate frame. To reduce the computational load, the proposed method first subsamples input images, and then block-wise motion estimation (ME) is performed. With the ME results, the proposed method detects scene changes and nontranslational GMs. This allows us to determine whether to proceed with the GM estimation and compensation processes. The geometric motion model is adopted to describe camera motions with four parameters, and these values are iteratively found on a motion vector (MV) field. Experimental results show that the proposed algorithm achieves significant performance improvements of subsequent FRC methods.

Frame-rate conversion using hybrid-search-based motion estimation and adaptive motion-compensated interpolation

We propose a frame-rate conversion algorithm using hybrid-search-based motion estimation (ME) and adaptive motion-compensated interpolation (MCI). The ME method uses three search strategies: recursive search, three-step search with predictions, and single predicted search. One of them, which is best suited for the predicted motion type, is adaptively performed on a block basis. This adaptation process improves the accuracy of the estimated motion vectors without increasing the computational load. With the estimated motion vectors, the proposed MCI method reconstructs high-quality frames, without producing block artifacts, by considering multiple motion trajectories. The method utilizes pixel smoothness constraints besides motion-vector reliability when creating and combining the multiple motion-compensated results to remove block artifacts in regions with unreliable motion vectors. Experimental results show that the proposed ME method produces reliable motion vectors that are closer to true motions. Also, the proposed MCI method achieves better image quality than existing algorithms.