In this paper, side information refinement methods for Wyner-Ziv video codec are presented. In the proposed method, each block of a Wyner-Ziv frame is separated into a predefined number of groups, and these groups are interleaved to be coded. The side information for the first group is generated by the motion compensated temporal interpolation using adjacent key frames only. Then, the side information for remaining groups is gradually refined using the knowledge of the already decoded signal of the current Wyner-Ziv frame. Based on this basic concept, two progressive side information refinement methods are proposed. One is the band-wise side information refinement (BW-SIR) method which is based on transform domain interleaving, while the other is the field-wise side information refinement (FW-SIR) method which is based on pixel domain interleaving. Simulation results show that the proposed methods improve the quality of the side information and rate-distortion performance compared to the conventional side information refinement methods.

A novel fast algorithm for shape matching using statistical features of shape contexts is presented. By pruning the candidate shapes using the moment-based statistical features of shape contexts, the required number of matching processes is dramatically reduced with negligible performance degradation. Experimental results demonstrate that the proposed algorithm reduces the pruning time up to 1/(r·n) compared with the conventional RSC algorithm while maintaining a similar or better performance, where n is the number of sampled points of a shape and r is the number of randomly selected representative shape contexts for the query shape.

Screen content generally consists of text, images, and videos variously generated or captured by computers and other electronic devices. For the purpose of coding such screen content, we introduce alternative intra prediction (AIP) modes based on the emerging high efficiency video coding (HEVC) standard. With text and graphics, edges are much sharper and a large number of corners exist. These properties make it difficult to predict blocks using a one-directional intra prediction mode. The proposed method provides two-directional prediction by combining the existing vertical and horizontal prediction modes. Experiments show that our AIP modes provide an average BD-rate reduction of 2.8% relative to HEVC for general screen contents, and a 0.04% reduction for natural contents.

A new motion field segmentation algorithm under the 8-parameters motion model is presented which uses a multipass iterative region-refining techinique. The iterative region-refining module consists of a seed block detection and subsequent region-refining iterations. An initial estimate of an object motion is provided in the seed block detection process. This initial estimate is iteratively updated and approaches to a reliable mapping parameter set in region-refining process. A multipass composition of the module makes it possible to detect multiple motions in a scene. Our simulation results confirm that the proposed method successfully partitions an image into independently moving objects with allowable computation time.

In this paper, we propose a method for extracting an object boundary from a low-quality image such as an infrared one. To take full advantage of a training set, the overall shape is modeled by incorporating statistical characteristics of moments into the point distribution model (PDM). Furthermore, a differential equation for the moment of overall shape is derived for shape refinement, which leads to accurate and rapid deformation of a boundary template toward real object boundary. The simulation results show that the proposed method has better performance than conventional boundary extraction methods.