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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.

DIBR(Depth Image Based Rendering) is a kind of view synthesis algorithm to generate images at free view points from the reference color image and its depth map. One of the main challenges of DIBR is the occurrence of holes that correspond to uncovered backgrounds at the synthesized view. In order to cover holes efficiently, two main approaches have been actively investigated. One is to develop preprocessing algorithms for depth maps or parallax maps to reduce the size of possible holes, and the other is to develop hole filling methods to fill the generated holes using adjacent pixels in non-hole areas. Most conventional preprocessing algorithms for reducing the size of holes are based on the smoothing process of depth map. Filtering of depth map, however, attenuates the resolution of depth map and generates geometric distortions. In this paper, we proposes a novel preprocessing algorithm for parallax map to improve the performance of hole-filling by avoiding the drawbacks of conventional methods.

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.

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.

In this paper, we propose new adaptive search range motion estimation methods where the search ranges are constrained by the probabilities of motion vector differences and a search point sampling technique is applied to the constrained search ranges. Our new methods are based on our previous work, in which the search ranges were analytically determined by the probabilities. Since the proposed adaptive search range motion estimation methods effectively restrict the search ranges instead of search point sampling patterns, they provide a very flexible and hardware-friendly approach in motion estimation. The proposed methods were evaluated and tested with JM16.2 of the H.264/AVC video coding standard. Experiment results exhibit that with negligible degradation in PSNR, the proposed methods considerably reduce the computational complexity in comparison with the conventional methods. In particular, the combined method provides performance similar to that of the hybrid unsymmetrical-cross multi-hexagon-grid search method and outstanding merits in hardware implementation.

A method for aggregating binary features to represent an image in a single vector is proposed. Binary features are decomposed into several sub-features to permit the individual aggregation of each sub-feature into a histogram. The method is completely quantisation-free, enabling exceptionally fast image representation. Experimental results demonstrate that the method is a competitive alternative for image representation when runtime efficiency is crucial.

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).

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.

This paper presents a new fast motion estimation method where search ranges are determined by the probabilities of motion vector differences (MVDs). The MVDs' distribution is investigated and its parameter is estimated by the maximum likelihood estimator. With the estimated distribution, the search ranges are efficiently restricted by a prefixed probability for MVDs. Experimental results show that the performance of the proposed method is very similar to that of the full search algorithm in PSNR but it enables significant reduction in the computational complexity.

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.

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.

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.

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. To reduce the complexity, in this paper, a fast intra prediction mode decision algorithm using directional gradients is proposed. The proposed algorithm is composed of 2-path structure. In the first path, 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 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.

In this paper, an algorithm to compensate the inter-view mismatch in the multi-view video according to the different characteristics of cameras is presented. Interview mismatches make it difficult to merge the multi-view images and decrease the coding performance. So, a preprocessing operation to compensate the mismatches is requisite in the multi-view video coding. It is shown that the mismatch in the outputs of multi-view cameras with different electro-optical transfer functions can be approximated with a linear model of a gain and an offset. In addition, a new algorithm for estimating and compensating the inter-view mismatch based on the detection of the overlapped region is presented. Experimental results using various rectified stereo images show that the proposed method compensates inter-view mismatches more accurately compared to the conventional approach.

We propose a rate control scheme using a rate-distortion (R-D) estimation model, which produces a consistent picture quality between consecutive frames. Our R-D estimation method offers a closed-form mathematical model that enables us to predict the bits and the distortion generated from a frame encoded at a given quantization parameter (QP) and vice versa. Its most attractive feature is its low computational complexity. Furthermore, it is accurate enough to be applied to practical video coding. In our simulation, the estimation errors for rate and distortion are less than 2.5% and 1.5%, respectively. Therefore, the proposed rate control scheme is appropriate for applications requiring low delay, low complexity, and the ability to control output bit-rate and quality accurately. Our scheme ensures that the video buffers do not underflow or overflow by satisfying the buffer constraint, and it also prevents quality difference between consecutive frames from exceeding a certain demanded level by adopting a distortion constraint. In addition, a consistent picture quality is maintained within a frame, and error propagation, caused by quality degradation of anchor frames, is reduced by differentiating the control procedure for anchor frames from that for nonanchor frames. Simulation results show that our control scheme achieves 0.52-1.84 dB peak signal-to-noise ratio (PSNR) gain over MPEG-2 Test Model 5 (TM5) rate control and maintains very consistent quality within a frame as well as between frames.

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·) 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.

In this paper, a frame rate up-conversion method that uses a texture adaptive motion estimation technique is proposed for reducing motion-blur problems in liquid crystal displays. Conventional bilateral motion estimation techniques have a major common drawback to hardly detect true motion vector if there are blocks with simple texture within the search range. To solve this problem, a texture adaptive bilateral motion estimation method that increases cost value of block with simple texture is proposed. Experimental results show that the proposed algorithm outperforms conventional methods in both objective and subjective image quality.

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.