An Efficient, Adaptive Unsharp Masking Based Interpolation for Video Intra Frame Up-sampling

Abstract

Most of the existing interpolation techniques available in the literature provide an undesirable blurring effect while converting a low resolution video to its high resolution counterpart. To overcome this problem, a new adaptive unsharp masking based interpolation technique is proposed here. The proposed method is a spatial domain pre-processing approach that extracts high frequency details in the spatial domain, sharpens the sub-sampled video to a certain degree through an adaptive selection of weight factor so as to compensate the blurring caused by the subsequent discrete cosine transform (DCT) based interpolation technique. The weight factor is determined adaptively for the first sub-sampled video frame by referring the corresponding original frame. Now, by exploiting the high correlation property of a video sequence, the same weight factor is utilized for sharpening the other sub-sampled video frames for restoring the fine details and critical edge information in the reconstructed video. Experimental results reveal that the proposed method shows an improvement up to 0.365 dB than DCT and a significant improvement up to 1.4 dB than the popular Bicubic interpolation technique in terms of average peak-signal-to-noise-ratio (PSNR) at 4:1 compression ratio with improved visual quality.