We propose new blur and blocking metrics and then present a no-reference image-quality assessment method using these blur and blocking metrics. To compute the blur metric, we first estimated a blur radius from a given image and its reblurred version by using edge differences and edge amplitudes. Because blurring in edge regions is generally more sensitive to human perception, the blur metric was estimated from the edge blocks. We also used kurtosis and structural similarity to better estimate the blur metric. To compute the blocking metric, the blocking artifact was modeled as a 2-D step function and the blockiness visibility was estimated by the brightness difference between adjacent blocks. After the blocky position was determined, the blocking metric was computed from the six differences between four adjacent blocks. Experimental results show that the objective quality scores correlated highly with the subjective quality scores.
We propose a new deinterlacing algorithm with selective motion compensation. It has been reported that deinterlacing methods using motion compensation produce significantly improved results, although they tend to yield undesired results in fast moving areas. This is due to weak correlations between the previous and current frames. The proposed algorithm solves this problem by selectively applying motioncompensated deinterlacing. We first apply intrafield interpolation in the spatial domain, and then selectively apply motion compensations according to the type of motion vectors. Experimental results show that the proposed method produces noticeably improved performance compared to existing motion-compensated deinterlacing methods.
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