The bulk of the video content available today over the Internet and over mobile networks suffers from many
imperfections caused during acquisition and transmission. In the case of user-generated content, which is typically
produced with inexpensive equipment, these imperfections manifest in various ways through noise, temporal
flicker and blurring, just to name a few. Imperfections caused by compression noise and temporal flicker are
present in both studio-produced and user-generated video content transmitted at low bit-rates. In this paper,
we introduce an algorithm designed to reduce temporal flicker and noise in video sequences. The algorithm takes
advantage of the sparse nature of video signals in an appropriate transform domain that is chosen adaptively based
on local signal statistics. When the signal corresponds to a sparse representation in this transform domain, flicker
and noise, which are spread over the entire domain, can be reduced easily by enforcing sparsity. Our results show
that the proposed algorithm reduces flicker and noise significantly and enables better presentation of compressed
videos.
KEYWORDS: Video, Transform theory, Super resolution, Linear filtering, Video compression, Visualization, Video processing, Multimedia, Computer programming, Electronic filtering
Multimedia services for mobile phones are becoming increasingly popular thanks to capabilities brought about
by location awareness, customized programming, interactivity, and portability. With mounting attraction to
these services there is desire to seamlessly expand the mobile multimedia experience to stationary environments
where high-resolution displays can offer significantly better viewing conditions. In this paper, we propose a
fast, high quality super-resolution algorithm that enables high resolution display of low-resolution video. The
proposed algorithm, SWAT, accomplishes sparse reconstructions using directionally warped transforms and spatially
adaptive thresholding. Comparisons are made with some existing techniques in terms of PSNR and visual
quality. Simulation examples show that SWAT significantly outperforms these techniques while staying within
a limited computational complexity envelope.
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