Partition Mode Research Articles

Coupled partition and configuration for cohesive and self-sufficient virtual microgrids

Active Distribution Network shows promise in operating and controlling distributed generations. Based on ADN structural and electrical differences, ADN could be partitioned to reveal innate heterogeneity for better control. Virtual Microgrid works as such a partition mode that each VM serves both as a part of ADN and as an individual operating agent. To identify VM boundaries, previous research utilized community detection methods and then configured ADN based on stationary partition. In this paper, VM partition and configuration are simultaneously determined in the proposed Coupled-Configuration-Partition of Active Planning. Also, a new definition of electrical edge betweenness is proposed to depict interaction among power system buses to improve the partition algorithm. Based on this proposal, ADN is planned to be cohesive in Net-ability, Flexibility Supply Index, and Capacity Fitness, in the meantime to minimize the cost in a multi-objective optimization, iteratively in Genetic Algorithm to reach Pareto Front. Case studies are designed to denote the benefits of the proposed Coupled-Configuration-Partition of Active Planning in modularity and system cohesion intensity.

Rapid CU Partitioning and Joint Intra-Frame Mode Decision Algorithm

H.266/Versatile Video Coding (VVC) introduces new techniques that build upon previous standards, proposing a nested multi-type tree quadtree (QTMT). The introduction of this structure significantly enhances video coding efficiency; additionally, the number of directional modes in H.266 has increased by 32 compared to H.265, accommodating a greater variety of texture patterns. However, the changes in the related structures have also led to a significant increase in encoding complexity. To address the issue of excessive computational complexity, this paper proposes a targeted rapid Coding Units segmenting approach combined with decision-making for an intra-frame modes algorithm. In the first phase of the algorithm, we extract different features for CU blocks of various sizes and input them into the decision tree model’s classifier for classification processing, determining the CU partitioning mode to prematurely terminate the partitioning, thereby reducing the encoding complexity to some extent. In the second phase of the algorithm, we put forward an intra-frame mode decision strategy grounded in gradient descent techniques with a bidirectional search mode. This maximizes the approach to the global optimum, thereby obtaining the optimal intra-frame mode and further reducing the encoding complexity. Experimentation has demonstrated that the algorithm achieves a 54.53% reduction in encoding time. In comparison, the BD-BR (Bitrate-Distortion Rate) only increases by 1.38%, striking an optimal balance between the fidelity of video and the efficacy of the encoding process.

Adaptive HEVC video steganograhpy based on PU partition modes

High EfficiencyVideoCoding (HEVC) −based steganography has gained attention as a prominent research focus. Especially, block structure based HEVC video steganography has received increasing attention due to commendable performance. However, current block structure- based steganography algorithms confront with challenges such as reduced coding efficiency and limited capacity. To avoid these problems, an adaptive video steganography algorithm based on Prediction Unit (PU) partition mode in I-frames is proposed. This is done through the analysis of the block division process and the visual distortion resulting from the modification of the PU partition mode in HEVC. The PU block structure is utilized as steganographic covers, and the Rate Distortion Optimization (RDO) technique is introduced to establish an adaptive distortion function for Syndrome-trellis code (STC). Further comparison is performed between the proposed method and the state-of-the-art steganography algorithms, confirming its advantages in embedding capacity, compression efficiency, visual quality, and resistance to video steganalysis.

Quantum nonlocality without entanglement in a 2n-partite system

In recent years, researchers focused their attention on the construction of nonlocal product states in multipartite quantum systems. This paper proposes a novel partitioning method for multipartite quantum systems, aiming to improve the operation efficiency. Firstly, we divide 2n subsystems into n parts two by two and implement orthogonality-preserving local measurement on the partitioned composite systems. Subsequently, based on the partitioning mode, nonlocal orthogonal product states in (C3)⊗6 and (C4)⊗6 are given. Finally, we construct nonlocal orthogonal product states in (Cd)⊗2n and discuss the cases where d is odd and even. Our results demonstrate the phenomenon of nonlocality without entanglement in a 2n-partite system.

Fast CU size decision and intra-prediction mode decision method for H.266/VVC

AbstractH.266/Versatile Video Coding (VVC) is the most recent video coding standard developed by the Joint Video Experts Team (JVET). The quad-tree with nested multi-type tree (QTMT) architecture that improves the compression performance of H.266/VVC is introduced. Moreover, H.266/VVC contains a greater number of intra-prediction modes than H.265/High Efficiency Video Coding (HEVC), totalling 67. However, these lead to extremely the coding computational complexity. To cope with the above issues, a fast intra-coding unit (CU) size decision method and a fast intra-prediction mode decision method are proposed in this paper. Specifically, the trained Support Vector Machine (SVM) classifier models are utilized for determining CU partition mode in a fast CU size decision scheme. Furthermore, the quantity of intra-prediction modes added to the RDO mode set decreases in a fast intra-prediction mode decision scheme based on the improved search step. Simulation results illustrate that the proposed overall algorithm can decrease 55.24% encoding runtime with negligible BDBR.

Three-dimensional motion vector translation for geometric partitioning mode in panoramic video coding using H.266/VVC

Three-dimensional motion vector translation for geometric partitioning mode in panoramic video coding using H.266/VVC

Adaptive HEVC Video Steganography With High Performance Based on Attention-Net and PU Partition Modes

With the increasing popularity of digital video, video steganography has become a hot research topic in the field of covert communication and privacy protection. The existing prediction unit (PU) based video steganography often tends to result in large bit rate increase, which is also easily noticeable to the steganography analyst. To solve this problem, an adaptive steganography for HEVC video based on attention-net and PU partition modes is proposed. First, the distortion of modified PUs is analyzed from the perspective of rate distortion optimization at the group of pictures (GOP) level, and we find that modifying PU will lead to distortion accumulation and abnormal bitrate increase. Therefore, an adaptive distortion function based on the improved rate distortion cost is designed, and the embedding distortion is minimized by using Syndrome-Trellis Code (STC) steganography coding. Meanwhile, a super-resolution convolutional neural network with non-local sparse attention-net filter is proposed to replace the in-loop filter in HEVC to reconstruct the reference frame, thereby reducing the bitrate cost and improving the visual quality of stego-video. Experimental results show that the proposed algorithm can achieve superior perceptual quality and bitrate performance comparing with the sate-of-the-art works.

Adaptive Merge Candidate Selection based on Geometric Partitioning Mode beyond Versatile Video Coding

Adaptive Merge Candidate Selection based on Geometric Partitioning Mode beyond Versatile Video Coding

An anti-steganalysis adaptive steganography for HEVC video based on PU partition modes

Calibration-based steganalysis methods are a fatal threat to current HEVC steganography algorithms. The current steganography algorithms based on prediction unit (PU) still lack resistance to this type of steganalysis. The reason is that the improvements in HEVC codec provide more potential steganographic space as well as introduce more detectable distortion. Therefore, a HEVC video steganography method that has resistance to PU shift-based steganalysis is proposed in this paper. First, the PU shift phenomenon that exists in PU-based steganography is introduced. Then the existing calibration-based steganalysis method is enhanced. According to the encoding rules of HEVC, the distortion cost function according to the difference of rate distortion cost (RD cost) during recompression is designed, which aims to obtain better resistance to calibration-based steganalysis. Experimental results demonstrate that the steganography algorithm designed in this paper provides excellent resistance ability for calibration-based steganalysis.

Paired decision trees for fast intra decision in H.266/VVC

The Versatile Video Coding (H.266/VVC) has demonstrated significant improvements to its predecessor High-Efficiency Video Coding (H.265/HEVC) in terms of compression efficiency. One of its advancements is attributed to the introduction of the QuadTree with nested Multi-type Tree (QTMT). However, the accompanying computing complexity challenges practical applications. To this end, this paper proposes a fast encoding algorithm to accelerate the encoding process of H.266/VVC while maintaining its compression efficiency as much as possible. Essentially, the Coding Unit (CU) partition can be modeled as a classification problem, and thus conventional machine learning methods such as the decision tree can be used to solve it. Previous decision tree-based works generally use one decision tree to dyadically select one partition from a set of candidate partition modes, easily leading to a local optimum. In contrast, this work models the classification from a new viewpoint and devises Paired Decision Trees (PDT) to tackle it. Specifically, the first decision tree determines whether to divide the current CU or not, and the second decision tree further determines whether to divide the CU horizontally or vertically. However, inaccurate results in the first decision tree can be propagated to the second one, resulting in error accumulation. To address this issue, we introduce the Indeterminate Space to both decision trees and perform Rate-Distortion Optimization (RDO) on samples falling within this space. Experimental results show that compared with H.266/VVC reference software VTM-15.0, the proposed PDT method saves 52.86% encoding time on average while maintaining a small BDBR increase of only 1.36%, which significantly outperforms previous works.

An efficient geometric partitioning mode by adaptive blending method for screen content coding

AbstractGeometric partitioning mode (GPM) was newly adopted in Versatile Video Coding (VVC) to increase the partitioning precision of the arbitrary boundaries of actual objects. Note that the blending process of the GPM is not suitable for screen content with various sharp and strong edges. To alleviate this drawback, three new GPM matrices are introduced, and an efficient GPM is proposed to utilize four different GPM matrices and implicitly determine one of them depending on the discontinuity and gradients of the two predictors. The proposed method achieves an average BD‐rate reduction of 1.2% and 1.6% without additional computational complexity for random access and low‐delay B configurations, respectively, over the VVC test model (VTM18.0).

A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List

Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%.

Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC

Compared with the previous generation of High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) introduces a quadtree and multi-type tree (QTMT) partition structure with nested multi-class trees so that the coding unit (CU) partition can better match the video texture features. This partition structure makes the compression efficiency of VVC significantly improved, but the computational complexity is also significantly increased, resulting in an increase in encoding time. Therefore, we propose a fast CU partition decision algorithm based on DenseNet network and decision tree (DT) classifier to reduce the coding complexity of VVC and save more coding time. We extract spatial feature vectors based on the DenseNet network model. Spatial feature vectors are constructed by predicting the boundary probabilities of 4 × 4 blocks in 64 × 64 coding units. Then, using the spatial features as the input of the DT classifier, through the classification function of the DT classifier model, the top N division modes with higher prediction probability are selected, and other division modes are skipped to reduce the computational complexity. Finally, the optimal partition mode is selected by comparing the RD cost. Our proposed algorithm achieves 47.6% encoding time savings on VTM10.0, while BDBR only increases by 0.91%.

Effectively removing the homodimer in bispecific antibodies by weak partitioning mode of anion exchange chromatography

Small amounts of by-products are nevertheless created during the recombinant production of IgG-like bispecific antibodies due to imbalanced chain expression and improper chain pairing, despite the employment of molecular strategy techniques to promote accurate pairing. Among them, homodimers represent the species that are more difficult to remove due to their physical and chemical properties being similar to the target antibody. Homodimer by-products are always produced even though various technologies can significantly increase the expression of heterodimers, so a robust purification process to recover high-purity heterodimers is required. Most of the chromatography methods commonly adopt the bind-and-elute mode or two-step to separate homodimers, which has numerous drawbacks such as prolonged process times and limited dynamic binding capacity. Flow-through mode of anion exchange is a frequently-used polishing step for antibodies, but it is typically regarded as being more effective for host-cell protein or host-cell DNA removal rather than other product-related impurities such as homodimers and aggregates. This paper demonstrated that single-step anion exchange chromatography allows high capacity and effective clearance of the homodimer byproduct to be simultaneously achieved, suggesting that weak partitioning was a better polishing strategy for achieving a high level of heterodimer purity. And robust operation range of anion exchange chromatography steps for homodimer removal was also developed by leveraging the design of experiments.

An early CU partition mode decision algorithm in VVC based on variogram for virtual reality 360 degree videos

360-degree videos have become increasingly popular with the application of virtual reality (VR) technology. To encode such kind of videos with ultra-high resolution, an efficient and real-time video encoder becomes a key requirement. The Versatile Video Coding (VVC) standard has good coding performance. However, it has pretty high computational complexity which increasing the application cost of 360-degree videos. Among them, the decision of the quadtree with nested multi-type tree (QTMT) partitioning structure is one of the time-consuming procedures. In this paper, based on the characteristics of 360-degree video with Equirectangular projection (ERP) format, the empirical variogram combined with Mahalanobis distance is introduced to measure the difference between the horizontal and vertical directions of the CU, and a fast partition algorithm is proposed. The experimental results show that the algorithm saves 32.13% of the coding time with only an increase of 0.66% in BDBR.

Feature generation based on relation learning and image partition for occluded person re-identification

In order to solve the challenging tasks of person re-identification(Re-ID) in occluded scenarios, we propose a novel approach which divides local units by forming high-level semantic information of pedestrians and generates features of occluded parts. The approach uses CNN and pose estimation to extract the feature map and key points, and a graph convolutional network to learn the relation of key points. Specifically, we design a Generating Local Part (GLP) module to divide the feature map into different units. Based on different occluded conditions, the partition mode of GLP has high flexibility and variability. The features of the non-occluded parts are clustered into an intermediate node, and then the spatially correlated features of the occluded parts are generated according to the de-clustering operation. We conduct experiments on both the occluded and the holistic datasets to demonstrate its effectiveness.

Focal Stack Image Compression Based on Basis-Quadtree Representation

In this paper, we propose an efficient compression scheme for focal stack images (FoSIs) based on a new basis-quadtree representation. In the new basis-quadtree representation, FoSIs are initially reorganized as co-located block groups in the depth dimension. In each group, selective basis blocks and adaptive quadtree partition are optimized to predict the focused or defocused co-located blocks by intra-group approximation. By solving a joint optimization problem, FoSIs can be sparsely represented by the optimal basis blocks, corresponding quadtree partition and approximation parameters, which will be compressed separately. Then, these basis blocks are stitched into several new frames (basis frames) according to their original locations and partition modes. Basis frames are compressed by our designed encoder, where the intra-group approximation is embedded into the high efficiency video coding (HEVC) encoder. Thus, the redundancies of basis blocks can be further eliminated. Finally, the approximation parameters are refined to suppress the amplified errors caused by introduced compression blur after basis frame coding. The refined parameters are compressed losslessly and multiplexed with the bitstream of the basis frames to ensure the reconstruction quality of FoSIs. Experiments on 12 test sequences demonstrate that the proposed scheme can obtain higher coding performance than the state-of-the-art comparison schemes. Specifically, the proposed scheme achieves up to 5.23 dB PSNR gains and 71.59% bitrate savings over the HEVC baseline scheme on sequences I03 and I05, respectively.

Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatial-temporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.

An Anti-Steganalysis HEVC Video Steganography With High Performance Based on CNN and PU Partition Modes

The steganography research of videos leads to excellent communication methods for transmitting secret message, and high efficiency video coding(HEVC) video is one popular steganographic carrier. This article proposes a prediction unit(PU) based wide residual-net steganography(PWRN) for HEVC videos. The visual quality distortion of modifying PUs is theoretically analyzed, which illustrates that modifying PUs only has a little negative effect on visual quality. Therefore, the data hiding method in this article allows to modify all types of PUs except for <inline-formula><tex-math notation="LaTeX">$2N\times 2N$</tex-math></inline-formula> to each other according to the secret data. In this way, high embedding efficiency is achieved, and the PU distributions in stego-videos can be kept similar to those of cover-videos, which is essential for resisting steganalysis. Meanwhile, a super-resolution convolutional neural network(CNN) with wide residual-net filter(WRNF) is proposed to replace the in-loop filter in HEVC for reconstructing I-pictures, which results in more precisely predicted P-pictures, and it further leads to less bitrate cost and better visual quality of stego-videos. The experimental results show that the proposed PWRN successfully resists the latest PU-targeted steganalysis algorithms, and compared with the state-of-the-art work, PWRN has achieved the lowest bitrate cost and the highest visual quality under the same capacity.

A VVC Video Steganography Based on Coding Units in Chroma Components with a Deep Learning Network

Versatile Video Coding (VVC) is the latest video coding standard, but currently, most steganographic algorithms are based on High-Efficiency Video Coding (HEVC). The concept of symmetry is often adopted in deep neural networks. With the rapid rise of new multimedia, video steganography shows great research potential. This paper proposes a VVC steganographic algorithm based on Coding Units (CUs). Considering the novel techniques in VVC, the proposed steganography only uses chroma CUs to embed secret information. Based on modifying the partition modes of chroma CUs, we propose four different embedding levels to satisfy the different needs of visual quality, capacity and video bitrate. In order to reduce the bitrate of stego-videos and improve the distortion caused by modifying them, we propose a novel convolutional neural network (CNN) as an additional in-loop filter in the VVC codec to achieve better restoration. Furthermore, the proposed steganography algorithm based on chroma components has an advantage in resisting most of the video steganalysis algorithms, since few VVC steganalysis algorithms have been proposed thus far and most HEVC steganalysis algorithms are based on the luminance component. Experimental results show that the proposed VVC steganography algorithm achieves excellent performance on visual quality, bitrate cost and capacity.