Geometrical wavelets have already proved their strength in approximation, compression, and denoising of piecewise constant and piecewise linear images. In this paper, we extend this family by introducing planelets toward an effective representation of indoor depth images. It uses a linear fractional model to capture non-linearity of depth values in the planar regions of the output images of Kinect-like sensors. A block-based compression framework based on planelet approximation is then presented, which uses quadtree decomposition along with spatial predictions as an effective intra-coding scheme. Compared with both classical geometric wavelets and some state-of-the-art image coding algorithms, our method provides desirable quality by explicitly representing edges and planar patches.

In this paper, we propose a novel block-based approach for global motion estimation (GME) and panorama image construction in encoded MPEG-1 video streams. Direct extraction of motion vectors (MV) from MPEG stream greatly improves efficiency of our method against pixel-based methods. However, some MVs in MPEG stream do not indicate real object or camera motion in the scene. Therefore, we introduce a reliability measure to discriminate real MVs from noisy or outlier MVs. In addition, an iterative reweighting process is applied to increase accuracy of GME. Finally, panorama image of several sequences is constructed by using estimated camera motion. Experiments show that the proposed method has high accuracy and can produce high quality panorama images for compressed videos faster than realtime.