With the rapid development of Unmanned Aerial Vehicle (UAV), the compression of video data captured by UAV has become a growing critical issue. However, most advanced coding schemes, like H.264 and HEVC, are oriented for common videos and thus cannot afford ideal coding efficiency when applied to UAV platform. Considering the characteristics of UAV video, much more improvement could be imposed onto current coding schemes to make full use of UAV's sensor information. In this paper, we exploit long term redundancy existing in the video data captured by cruise UAV. Firstly, we establish a long-term wide-range background set for reference. Then we separate each frame into new-area part and overlapped part. Lastly, we use GPS information of each frame to get reference from background set and compress two parts individually. In the experiments, by comparing to standard HEVC, our method has given more than 20% reduction in bitrate and meanwhile more than 4% gain in PSNR.

In order to promote the development of auto-stereoscopic display, MPEG has proposed multi-view plus depth (MVD) format. The depth video is encoded and transmitted with color video to synthesize virtual views at the receiver side. The existing video coding standards such as H.264/AVC introduces coding artifacts along the depth boundaries, which may seriously affects the synthesized view quality and coding efficiency. Many in-loop depth filters such as joint depth filter have been proposed to remove the artifacts in compressed depth video. However, their performance is unstable and affected by the outliers due to the weighted summation. In this paper, based on the sparse prior characteristic in local region of depth map, we propose a joint weighted sparse representation based median filter to select the most relevant neighboring depth pixel as the output during the filter process. Experimental results show the proposed method is more effective in improving the depth video coding efficiency.

In this paper, we exploit a new type of data redundancy in the multisource surveillance video to reduce the huge gap between the growth rate of the data and the video compression rate. Global redundancy caused by correlated appearances of moving objects in multiple videos consists of model similarity, spatial correlation and temporal consistency. Therefore, we propose a global coding scheme of moving objects to eliminate the global redundancy: a model based object reconstruction is initially employed to reconstruct the objects in the video, then a pose-based residual error prediction is developed to compensate the difference between the real video appearance and the initial reconstruction from model. The experiment with two simulated surveillance videos has proved that the proposed coding scheme can achieve better coding performance than the main profile of HEVC and surveillance profile of IEEE 1857-2013.

Background model can help to improve the compression efficiency for surveillance video coding, but the existing frame-based background model is inefficient in some situations, for example, when a region of background changes frequently or periodically. In this paper, a block-based background model is proposed to solve this problem. We save the background blocks recognized from each reconstructed frame into a buffer, thus the background blocks are collected gradually. At the same time, we compose a new background frame for each frame to be encoded based on the background blocks currently available in the buffer. Compared with the pre-built background frame, the instantly composed background frame often predicts more accurately because of the accumulated information about background. Experimental results show that the proposed model achieves better rate-distortion performance over the existing frame-based model in most cases, while keeping almost the same computation complexity.