Video coding focuses on reducing the data size of videos. Video stabilization targets at removing shaky camera motions. In this paper, we enable video coding for video stabilization by constructing the camera motions based on the motion vectors employed in the video coding. The existing stabilization methods rely heavily on image features for the recovery of camera motions. However, feature tracking is time-consuming and prone to errors. On the other hand, nearly all captured videos have been compressed before any further processing and such a compression has produced a rich set of block-based motion vectors that can be utilized for estimating the camera motion. More specifically, video stabilization requires camera motions between two adjacent frames. However, motion vectors extracted from video coding may refer to non-adjacent frames. We first show that these non-adjacent motions can be transformed into adjacent motions such that each coding block within a frame contains a motion vector referring to its adjacent previous frame. Then, we regularize these motion vectors to yield a spatially-smoothed motion field at each frame, named as CodingFlow, which is optimized for a spatially-variant motion compensation. Based on CodingFlow, we finally design a grid-based 2D method to accomplish the video stabilization. Our method is evaluated in terms of efficiency and stabilization quality, both quantitatively and qualitatively, which shows that our method can achieve high-quality results compared with the state-of-the-art methods (feature-based).

Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems. In this paper, we present an efficient method to remove rain streaks. First, the location map of rain pixels needs to be known as precisely as possible, to which we implement a relatively accurate detection of rain streaks by utilizing two characteristics of rain streaks.The key component of our method is to represent the intensity of each detected rain pixel using a linear model: p = αs+β, where p is the observed intensity of a rain pixel and s represents the intensity of the background (i.e., before rain-affected). To solve α and β for each detected rain pixel, we concentrate on a window centered around it and form an L2-norm cost function by considering all detected rain pixels within the window, where the corresponding rain-removed intensity of each detected rain pixel is estimated by some neighboring non-rain pixels. By minimizing this cost function, we determine α and β so as to construct the final rainremoved pixel intensity. Compared with several state-of-the-art works, our proposed method can remove rain streaks from a single color image much more efficiently -it offers not only a better visual quality but also a speed-up of several times to one degree of magnitude.

No abstract

No abstract

No abstract