Automated detection and segmentation of every nucleus in microscopy images is a fundamental task in biomedical research and clinical practice, including nuclear morphology analysis, cancer grading and prognosis predicting. However, this task is still challenging because of the nuclear atypical in shape, size and internal organisation. In this paper, we propose a nuclei instance segmentation method with the aim of jointing detection and segmentation simultaneously. The method builds on a two-stream Convolutional Neural Network(CNN) architecture that explicit utilize a single shot multi-box detector with Feature Pyramid Network in one stream for obtaining the nuclear location information and keep an U-net in the other stream for nuclei instance segmentation. Furthermore, the recurrent, residual and attention mechanisms are integrated for focusing on the useful information. The approach utilizes the strengths of the Recurrent Convolutional Neural Network (RCNN), the Attention Network and the Residual Network. The experiments demonstrate that the proposed nuclei instance segmentation approach outperforms prior state-of-the-art methods, and could be generalized across variety of nuclear type, magnification and imaging modality.

Motion-compensated frame interpolation (MCFI), a frame-interpolation technique to increase the motion continuity of low frame-rate video, can be utilized by counterfeiters for faking high bitrate video or splicing videos with different frame rates. For existing MCFI detectors, their performances are degraded under real-world scenarios such as H.264/AVC compression, noise, or blur. To address this issue, a robust MCFI detector is proposed to locate interpolated frames. By analyzing the distribution of residual energies within interpolated frames, we observe that there exist strong correlations between artifact regions and high residual energies. Thus, an artifact indicated map is introduced to select candidate artifact regions. Then, Tchebichef moments (TMs) are exploited to characterize the blurring effects or deformed structures among these regions. Specifically, the mean value of absolute high-order TMs of selected regions is used to model these temporal inconsistencies. Finally, a sliding window is adopted to locate interpolated frames, which are further refined by three post-processing operations. Chrominance information is also integrated with luminance information for robust identification of interpolated frames. Extensive experimental results show that compared with the state-of-the-art MCFI detectors, the proposed approach is more robust for compressed videos under various real-world scenarios.

Motion-compensated frame rate up-conversion (MC-FRUC) is originally presented to increase the motion continuity of low frame rate videos by periodically inserting new frames, which improves the viewing experience. However, MC-FRUC can also be exploited to fake high frame rate videos or splice two videos with different frame rates for malicious purposes. A blind forensics approach is proposed for the identification of various MC-FRUC techniques. A theoretical model is first built for residual signal, which is exploited as tampering trace for blind forensics. The identification of various MC-FRUC techniques is then converted into a problem of discriminating the differences of residual signals among them. A pre-classifier is designed to suppress the side effects of original frames and static interpolated frames in candidate videos. Then, spatial and temporal Markov statistics features are extracted from the residual signals inside the interpolated frames for MC-FRUC identification. Five open MC-FRUC softwares and six representative MC-FRUC techniques have been tested, and experimental results show that the proposed approach can effectively locate interpolated frames and further identify the adopted MC-FRUC technique for both uncompressed videos and compressed videos with high perceptual qualities.

As a 3D extension to the High Efficiency Video Coding (HEVC) standard, 3D-HEVC was developed to improve the coding efficiency of multiview videos. It inherits the prediction modes from HEVC, yet both Motion Estimation (ME) and Disparity Estimation (DE) are required for dependent views coding. This improves coding efficiency at the cost of huge computational costs. In this article, an early Merge mode decision approach is proposed for dependent texture views and dependent depth maps coding in 3D-HEVC based on priori and posterior probability models. First, the priori probability model is established by exploiting the hierarchical and interview correlations from those previously encoded blocks. Second, the posterior probability model is built by using the Coded Block Flag (CBF) of the current coding block. Finally, the joint priori and posterior probability model is adopted to early terminate the Merge mode decision for both dependent texture views and dependent depth maps coding. Experimental results show that the proposed approach saves 45.2% and 30.6% encoding time on average for dependent texture views and dependent depth maps coding while maintaining negligible loss of coding efficiency, respectively.

High-efficiency video coding (HEVC) can greatly improve coding efficiency compared with the prior video coding standard H.264/AVC by adopting advanced hierarchical coding structures such as coding unit (CU), prediction unit (PU), and transform unit. For each CU, an exhaustive mode decision strategy is adopted to achieve the best rate distortion (RD) cost, which simultaneously results in enormous computational complexity. In this paper, an early SKIP mode decision algorithm is proposed for the HEVC encoder to speed up the process of mode decision. Each CU size is categorized into either rare used or frequent used by exploiting the correlation of CU depth, which is estimated from the temporally colocated CUs. For the rare-used CU size, the SKIP mode is directly selected as the optimal mode and the remaining mode decision process is early terminated. For the frequent-used CU size, a unimodal stopping model is designed for its early SKIP mode decision by exploiting both hierarchical mode structure and RD cost property. Experimental results show that the proposed early SKIP mode decision method achieves average 58.5% and 54.8% encoding time savings, while the Bjontegaard Delta bit rate only increases average 0.8% and 0.8% for various test sequences under the random access and the low delay B conditions, respectively.