The Multiview Video Coding (MVC) standard is currently under development by the Joint Video Team as an extension of the Advanced Video Coding (H.264/AVC) standard. An MVC encoder compresses more than one viewpoint of a scene captured by different cameras. Redundancies between views can be used for inter-view prediction in encoding as well as error concealment in decoding. In this paper, a new algorithm utilizing motion information of pictures from other views to conceal a lost picture is proposed. The algorithm first derives motion information for a lost picture based on motion fields of pictures in adjacent views. Then, traditional motion compensation is invoked within the view containing the lost picture to derive a concealed frame. Experimental results show that the proposed algorithm can improve video quality with a negligible computational complexity overhead compared to simple temporal error concealment algorithms.

We present in this paper a novel distributed image coding scheme by exploiting image spatial correlation via Markov random field modeling at the decoding end. This allows us to design a simple yet efficient encoder suitable for various energy efficient imaging sensor network applications. The novelty is the integration of LDPC decoding and Markov random field modeling in order to jointly exploit both interimage and intra-image correlation. The current research aims at improving our previous work in which the Markov model was defined by a state transition probability matrix. In this research, we model the image via a Markov random field described by Gibbs distribution. Both analysis and simulations have been carried out to demonstrate that this Markov model-based approach is able to achieve significant gains over the schemes without Markov modeling. Furthermore, this new Gibbs-based Markov model is less sensitive to correlated noise. Our approach also outperforms a JPEG codec by up to 4 dB even if the interimage correlation is not very high.

Error concealment techniques are very important for video communication since compressed video sequences may be corrupted or lost when transmitted over error-prone networks. In this paper, we propose a novel two-stage error concealment scheme for erroneously received video sequences. In the first stage, we propose a novel spatio-temporal boundary matching algorithm (STBMA) to reconstruct the lost motion vectors (MV). A well defined cost function is introduced which exploits both spatial and temporal smoothness properties of video signals. By minimizing the cost function, the MV of each lost macroblock (MB) is recovered and the corresponding reference MB in the reference frame is obtained using this MV. In the second stage, instead of directly copying the reference MB as the final recovered pixel values, we use a novel partial differential equation (PDE) based algorithm to refine the reconstruction.We minimize, in a weighted manner, the difference between the gradient field of the reconstructed MB in current frame and that of the reference MB in the reference frame under given boundary condition. A weighting factor is used to control the regulation level according to the local blockiness degree. With this algorithm, the annoying blocking artifacts are effectively reduced while the structures of the referenceMBare well preserved. Compared with the error concealment feature implemented in the H.264 reference software, our algorithm is able to achieve significantly higher PSNR as well as better visual quality.

Recently, image-based, high throughput RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Effective automated segmentation technique is significant in analysis of RNAi images. However, graph cuts based active contour (GCBAC) method needs interaction during segmentation. Here, we present a novel approach to overcome this shortcoming. The process consists the following steps: First, region-growing algorithm uses extracted nuclei to get the initial contours for segmentation of cytoplasm. Then, constraint factor obtained from binary segmentation of enhanced image is incorporated to improve the performance of cytoplasm segmentation. Finally, morphological thinning algorithm is implemented to solve the touching problem of clustered cells. Our approach can automatically segment clustered cells with polynomial time-consuming. The excellent results verify the effectiveness of the proposed approach.

This paper proposes a video coding scheme, in which textural and structural regions are selectively removed in the encoder, and restored in the decoder by spatio-temporal texture ynthesis and edge-based inpainting. In the proposed scheme, two types of regions are classified based on two motion models: local motion and global motion. In local motion regions, conventional blockbased motion estimation is employed for region removal and spatio-temporal texture synthesis is applied for recovery of the removed regions. In global motion regions, edge-based image inpainting is utilized to recover removed regions, and sprite generation is used as an auxiliary tool to keep temporal consistency. In the proposed scheme, both structures and textures are handled and some kinds of assistant information which can guide restoration are extracted and coded. This approach is blockbased and thus is flexible and generic to be implemented into standard-compliant video coding schemes. It has been implemented into H.264/AVC and achieves up to 35% bitrate saving at similar visual quality levels compared with H.264/AVC without our approach.