Abstract
This paper proposes an accurate deinterlacing algorithm using a maximum a posteriori (MAP) estimator. First, we produce accurate motion vector fields between the current field and adjacent fields by employing an advanced motion compensation scheme that is suitable for an interlaced format. Next, the progressive frame corresponding to the current field is found via the MAP estimator based on the derived motion vector fields. Here, in order to obtain a stable solution, well-known bilateral total variation–based regularization is applied. Then, at a specific mode decision step, it is decided whether the result from the aforementioned temporal deinterlacing is acceptable or not. Finally, if the temporal deinterlacing is determined to be inappropriate by the mode decision, a typical spatial deinterlacing is applied instead of the MAP estimator-based temporal deinterlacing. Experimental results show that the proposed algorithm provides at maximum 2 dB higher PSNR than a cutting-edge deinterlacing algorithm, while providing better visual quality than the latter.
Highlights
Deinterlacing is an important technique because it converts interlaced video sequences into progressive sequences for progressive digital display devices, such as LCDs, plasma display panels, and organic light-emitting diode TVs
This paper presents a stronger mode decision method that takes into account mean of absolute differences (MAD) values, motion vector (MV) correlation, and MAD correlation
This paper presents a robust temporal deinterlacing algorithm based on an maximum a posteriori (MAP) estimator
Summary
Deinterlacing is an important technique because it converts interlaced video sequences into progressive sequences for progressive digital display devices, such as LCDs, plasma display panels, and organic light-emitting diode TVs. Of the superresolution image may be obtained such that, when reprojected back into the images via a generative imaging model, it minimizes the difference between the actual and predicted observations.[24] Note that if accurate motion information of a certain missing pixel is given, the MAP-based superresolution can reconstruct the missing pixel such that it is very close to its original. We propose an advanced MAP-estimator-based deinterlacing algorithm using high-performance MC method and strong mode decision. If the temporal deinterlacing is determined to be inappropriate by the mode decision, a typical spatial deinterlacing based on edge-directional interpolation is applied instead of the MAP estimatorbased temporal deinterlacing. Experimental results show that the proposed algorithm obtains at maximum 2 dB higher peak signal-to-noise ratio (PSNR) than the state-of-the-art spatiotemporal deinterlacing algorithm, i.e., Fan’s algorithm,[17] while providing better visual quality.
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