In low bit-rate packet-based video communications, video frames may have very small size, so that each frame fills the payload of a single network packet; thus, packet losses correspond to whole-frame losses, to which the existing error concealment algorithms are badly suited and generally not applicable. In this paper, we deal with the problem of concealment of whole frame-losses, and propose a novel technique which is capable of handling this very critical case. The proposed technique presents other two major innovations with respect to the state-of-the-art: i) it is based on optical flow estimation applied to error concealment and ii) it performs multiframe estimation, thus optimally exploiting the multiple reference frame buffer featured by the most modern video coders such as H.263+ and H.264. If data partitioning is employed, by e.g., sending headers, motion vectors, and coding modes in prioritized packets as can be done in the DiffServ network model, the algorithm is capable of exploiting the motion vectors to improve the error concealment results. The algorithm has been embedded in the H.264 test model software, and tested under both independent and correlated packet loss models with parameters typical of the wireless environment. Results show that the proposed algorithm significantly outperforms other techniques by several dBs in peak signal-to-noise ratio (PSNR), provides good visual quality, and has a rather low complexity, which makes it possible to perform real-time operation with reasonable computational resources.

We propose a novel image and video preprocessing technique (edge and residuum decomposition - ERD) that mainly concerns the decomposition of the image into three separate components: edge location; gray-tone or color inside the edges; residuum "smooth" image. While the residuum image is compatible with any natural-image encoder, new algorithms and solutions are introduced to achieve a compressed description of the edge-based information; the overall occupancy is comparable to that yielded by traditional image and video encoders. Both qualitative and quantitative results are shown, as well as an analysis of the separate components. Since all the components (edge position, edge color and residuum) are needed to completely describe the image, whilst some of them are particularly critical to recognize (and "preview") it, the proposed solution enables new encoding services, mainly selective encryption; just hiding edge positions leaves a perceptually meaningless image unencrypted.

In this paper, we propose a joint despeckling and edge detection algorithm based on the Mumford-Shah functional, which accomplishes the image filtering and segmentation as a result of an analytical variational problem. This approach turns out to be well suited to jointly despeckle and segment SAR image data; the experimental results demonstrate that the proposed technique yields high quality despeckling without impairing critical image features and with the additional advantage to provide a detailed edge map

We propose a spatio-temporal error concealment algorithm for video transmission in an error-prone environment. The proposed technique employs motion vector estimation, edge-preserving interpolation, and texture analysis/synthesis. It has two main advantages with respect to existing methods, namely: (i) it aims at optimizing the visual quality of the restored video, and not only PSNR; and (ii) it employs an automatic mode selection algorithm in order to decide, on a macroblock basis, whether to use the spatial restoration, the temporal one, or a combination thereof. The algorithm has been applied to H.26L video, providing satisfactory performance over a large set of operating conditions.

A known problem in video streaming is that loss of a packet usually results into loss of a whole video frame. In this paper we propose an error concealment algorithm specifically designed to handle this sort of losses. The technique exploits information in a few past frames (namely the motion vectors) in order to estimate the forward motion vectors of the last received frame. This information is used to project the last frame onto an estimate of the missing frame. The algorithm has been tested on MPEG-2 video, providing very satisfactory results, and outperforming by several dBs in PSNR the concealment technique based on repetition of the last received frame.

We proposed a spatio-temporal error concealment algorithm for video transmission in an error-prone environment. The proposed technique employs motion vector estimation, edge-preserving interpolation, and texture analysis/synthesis. It has two main advantages with respect to existing methods, namely: i) it aims at optimizing the visual quality of the restored video, and not only PSNR; and ii) it employs an automatic mode selection algorithm in order to decide, on a macroblock basis, whether to use the spatial restoration, the temporal one, or a combination thereof. The algorithm has been applied to H26L video, providing satisfactory performance over a large set of operating conditions.

In this paper we propose a novel error concealment algorithm for video transmission over wireless networks potentially subject to packet erasures. In particular, we develop a technique for the replenishment of missing macroblocks, which aims at minimizing the impact of the lost data on the resulting video with respect to the human visual system. The proposed algorithm operates three reconstruction stages at different scales, by first recovering smooth large-scale patterns, then large-scale structures, and finally local edges in the lost macroblock. Experimental results show that the proposed algorithm achieves improved visual quality of the reconstructed frames with respect to other state-of-the-art techniques, as well as better PSNR results.