Chan-Hee Han

Adaptive Depth Fusion based on Reliability of Depth Cues for 2D-to-3D Video Conversion

3D video is regarded as the next generation contents in numerous applications. The 2D-to-3D video conversion technologies are strongly required to resolve a lack of 3D videos during the period of transition to the full ripe 3D video era. In 2D-to-3D conversion methods, after the depth image of each scene in 2D video is estimated, stereoscopic video is synthesized using DIBR (Depth Image Based Rendering) technologies. This paper proposes a novel depth fusion algorithm that integrates multiple depth cues contained in 2D video to generate stereoscopic video. For the proper depth fusion, it is checked whether some cues are reliable or not in current scene. Based on the result of the reliability tests, current scene is classified into one of 4 scene types and scene-adaptive depth fusion is applied to combine those reliable depth cues to generate the final depth information. Simulation results show that each depth cue is reasonably utilized according to scene types and final depth is generated by cues which can effectively represent the current scene.

Fast Intra Mode Decision Algorithm Using Directional Gradients for H.264

H.264/AVC video coding standard uses the rate distortion optimization method which determines the best coding mode for macroblock(MB) to improve coding efficiency. Whereas RDO selects the best coding mode, it causes the heavy computational burden comparing with previous standards. In this paper, a fast intra prediction mode decision algorithm using two-path structure is proposed. In the first path, 16x16 intra prediction mode is determined using directional gradients. In the second path, 3 modes instead of 9 modes are chosen for RDO to decide the best mode for 4x4 block. Finally, the two modes determined in the two-path decision process are compared to decide the final block mode. Experimental results show that the computation time of the proposed method is decreased to about 77% of the exhaustive mode decision method with negligible quality loss. Keywords-component; H.264 video coding, Intra prediction

Improved Side Information Generation using Field Coding for Wyner-Ziv Codec

Wyner-Ziv video coding is a new video compression paradigm based on distributed source coding theory of Slepian-Wolf and Wyner-Ziv. Wyner-Ziv coding enables light-encoder/heavy-decoder structure by shifting complex modules including motion estimation/compensation task to the decoder. Instead of performing the complicated motion estimation process in the encoder, the Wyner-Ziv decoder performs the motion estimation for the generation of side information in order to make the predicted signal of the Wyner-Ziv frame. The efficiency of side information generation deeply affects the overall coding performance, since the bit-rates of the Wyner-Ziv coding is directly dependent on side information. In this paper, an improved side information generation method using field coding is proposed. In the proposed method, top fields are coded with the existing SI generation method and bottom fields are coded with new SI generation method using the information of the top fields. Simulation results show that the proposed method improves the quality of the side information and rate-distortion performance compared to the conventional method.

Low-complexity depth map generation for real-time 2D-to-3D video conversion

A low-complexity depth generation algorithm for 3D conversion of 2D videos is presented. For temporal consistency in global depth, a pattern-based depth generation method is introduced. In addition, refinement of depth from the combined depth cues of texture and motion is also addressed. Experimental results show that the proposed method outperforms previous algorithms in terms of the complexity and subjective quality.

Efficient Cell Tracking Method for Automatic Analysis of Cellular Sequences

The tracking and analysis of cell activities in time-lapse sequences plays an important role in understanding complex biological processes such as the spread of the tumor, an invasion of the virus, the wound recovery and the cell division. For automatic tracking of cells, the tasks such as the cell detection at each frame, the investigation of the correspondence between cells in previous and current frames, the identification of the cell division and the recognition of new cells must be performed. This paper proposes an automatic cell tracking algorithm. In the first frame, the marker of each cell is extracted using the feature vector obtained by the analysis of cellular regions, and then the watershed algorithm is applied using the extracted markers to produce the cell segmentation. In subsequent frames, the segmentation results of the previous frame are incorporated in the segmentation process for the current frame. A combined criterion of geometric and intensity property of each cell region is used for the proper association between previous and current cells to obtain correct cell tracking. Simulation results show that the proposed method improves the tracking performance compared to the tracking method in Cellprofiler (the software package for automatic analysis of bioimages).

Improved Side Information Generation Using Field Coding for Wyner-Ziv Codec

Wyner-Ziv video coding is a new video compression paradigm based on distributed source coding theory of Slepian- Wolf and Wyner-Ziv. Wyner-Ziv coding enables light- encoder/heavy-decoder structure by shifting complex modules including motion estimation and compensation task to the decoder. Instead of performing the complicated motion estimation process in the encoder, the Wyner-Ziv decoder performs the motion estimation for the generation of side information in order to make the predicted signal of the Wyner- Ziv frame. The efficiency of side information generation deeply affects the overall coding performance, since the bit-rates of the Wyner-Ziv coding is directly dependent on side information. In this paper, an improved side information generation method using field coding is proposed. In the proposed method, top fields are coded with the existing SI generation method and bottom fields are coded with new SI generation method using the information of the top fields. Simulation results show that the proposed method improves the quality of the side information and rate-distortion performance compared to the conventional method. (Abstract)

Residual DPCM in HEVC Transform Skip Mode for Screen Content Coding

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode’s Bjontegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

Optimal Coding Model for Screen Contents Applications from the Coding Performance Analysis of High Efficient Coding Tools in HEVC

Screen content refers to images or videos generated by various electronic devices such as computers or mobile phones, whereas natural content refers to images captured by cameras. Screen contents show different statistical characteristics from natural images, so the conventional video codecs which were developed mainly for the coding of natural videos cannot guarantee good coding performances for screen contents. Recently, researches on efficient SCC(Screen Content Coding) are being actively studied, and especially at ongoing JCT-VC(Joint Collaborative Team on Video Coding) meeting for HEVC(High Efficiency Video Coding) standard, SCC issues are being discussed steadily. In this paper, we analyze the performances of high efficient coding tools in HM(HEVC Test Model) on SCC, and present an optimized SCC model based on the analysis results. We also present the characteristics of screen contents and the future research issues as well.

Preprocessing Algorithm of Cell Image Based on Inter-Channel Correlation for Automated Cell Segmentation

The automated segmentation technique of cell region in Bio Images helps biologists understand complex functions of cells. It is mightly important in that it can process the analysis of cells automatically which has been done manually before. The conventional methods for segmentation of cell and nuclei from multi-channel images consist of two steps. In the first step nuclei are extracted from DNA channel, and used as initial contour for the second step. In the second step cytoplasm are segmented from Actin channel by using Active Contour model based on intensity. However, conventional studies have some limitation that they let the cell segmentation performance fall by not considering inhomogeneous intensity problem in cell images. Therefore, the paper consider correlation between DNA and Actin channel, and then proposes the preprocessing algorithm by which the brightness of cell inside in Actin channel can be compensated homogeneously by using DNA channel information. Experiment result show that the proposed preprocessing method improves the cell segmentation performance compared to the conventional method.

Automatic Segmentation of Cellular Images for High-Throughput Genome-Wide RNA Interference Screening

In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.