Embedded Graph Representation for Inter-Frame Coding of Dynamic Meshes

This article presents a new compression scheme for 3D dynamic meshes, referred to as Video and Subdivision based Mesh Coding (VSMC). The VSMC approach combines a displaced subdivision surface model with video-based coding in order to achieve efficient compression performance and real-time, low-power decoding and playback. In addition, VSMC supports a rich set of functionalities including scalability (spatial, temporal, and quality) and progressive transmission. The proposed scheme [1] was shown to outperform the anchor for the MPEG Call for Proposals on Dynamic Mesh coding [2] and was recently selected by the ISO MPEG 3D Graphics Coding group as the basis for the upcoming Video-based Dynamic Mesh Coding standard.

Dynamic point clouds and meshes are used in a wide variety of applications such as gaming, visualization, medicine, and more recently AR/VR/MR. This paper presents two extensions of MPEG-I Video-based Point Cloud Compression (V-PCC) standard to support mesh coding. The extensions are based on Edgebreaker and TFAN mesh connectivity coding algorithms implemented in the Google Draco software and the MPEG SC3DMC software for mesh coding, respectively. Lossless results for the proposed frameworks on top of version 8.0 of the MPEG-I V-PCC test model (TMC2) are presented and compared with Draco for dense meshes.

ISO/IEC JTC1 SC29, also called MPEG, has been working on a compression standard for dynamic meshes since couple of year now and it has released a Call for Proposals (CfP) for Dynamic Mesh Coding in October 2021. One of the goals of the future standard is to utilize the Visual Volumetric Video-based Coding (V3C) framework, defined in ISO/IEC 23090-5, that is already used for dynamic point cloud compression and volumetric video. In this paper, the authors described their vision of how dynamic mesh compression could be achieved, which corresponds to their technical response to the CfP. The presented objective and subjective results will show that the proposed solution outperforms the anchor in terms of objective metrics and subjective perceived visual quality for low bit rate use cases.

Laplacian mesh compression, also known as high‐pass mesh coding, is a popular technique for efficiently storing both static and dynamic triangle meshes that gained further recognition with the advent of perceptual mesh distortion evaluation metrics. Currently, the usual rule of thumb that drives the decision for a mesh compression algorithm is whether or not accuracy in absolute scale is required: Laplacian mesh encoding is chosen when perceptual quality is the main objective, while other techniques provide better results in terms of mechanistic error measures such as mean squared error.In this work, we present a modification of the Laplacian mesh encoding algorithm that preserves its benefits while it substantially reduces the resulting absolute error. Our approach is based on analyzing the reconstruction stage and modifying the quantization of differential coordinates, so that the decoded result stays close to the input even in areas that are distant from anchor points. In our approach, we avoid solving an overdetermined system of linear equations and thus reduce data redundancy, improve conditioning and achieve faster processing. Our approach can be directly applied to both static and dynamic mesh compression and we provide quantitative results comparing our approach with the state of the art methods.

We study normal factor graph (NFG) representations of stabilizer quantum error-correction codes (QECCs), in particular NFG representations of the stabilizer label code and the normalizer label code associated with a stabilizer QECC. The structure of the NFGs we are using is such that the (symplectic) self-orthogonality constraint that stabilizer label codes have to satisfy can be proven rather straightforwardly by applying certain NFG reformulations. We show that a variety of well-known stabilizer QECCs can be expressed in this framework: (tail-biting) convolutional stabilizer QECCs, the toric stabilizer QECCs by Kitaev, and a class of stabilizer QECCs that was recently introduced by Tillich and Zemor. Our approach not only gives new insights into these stabilizer QECCs, but will ultimately help to formulate new classes of stabilizer QECCs and low-complexity (approximate) decoding algorithms.

A Survey on Coding of Static and Dynamic 3D Meshes

Learning the fine-grained code representation for log-level prediction

Survey of source code vulnerability analysis based on deep learning

Automatic ICD coding, as a fundamental task in the field of healthcare management, has been paid much attention by researchers. However, the current deep learning-based ICD coding research mostly focus on the introduction of external diagnostic description text or the imposition of rules, while ignoring the structured features of the coding text itself. Especially for short texts of medical procedure codes, it is much more important to mine the information value in the texts. In this paper, we propose a structure-enhanced unsupervised semantic graph representation for precise medical procedure coding (SMP-Graph). The SMP-Graph representation method constructs each medical procedure text with an inductive heterogeneous graph and particularly enhances the kernel knowledge by extracting the axis words and chapter title and allocating them with distinct node representations. Both the nodes and edges are generated by the unsupervised pretrained model and then interact information in a bidirectionally weighted graph structure. Therefore, the SMP-Graph really realizes the intra-integration of unsupervised contextualized information and graph-based global information from the medical procedure code. Experiments conducted on the Chinese ICD-9-CM-3 procedure text dataset we collected from EMRs demonstrate that the SMP-Graph is a better representation method that outperforms other representative methods for medical procedure coding. Characteristic analysis is also conducted to prove the interpretability and adaptability of the SMP-Graph on the medical procedure coding task.

The explosive growth of vulnerabilities poses a significant threat to the security of software systems. While various deep-learning-based vulnerability detection methods have emerged, they primarily rely on semantic features extracted from a single code representation structure, which limits their ability to detect vulnerabilities hidden deep within the code. To address this limitation, we propose S2FVD, short for Sequence and Structure Fusion-based Vulnerability Detector, which fuses vulnerability-indicative features learned from the multiple views of the code for more accurate vulnerability detection. Specifically, S2FVD employs either well-matched or carefully extended neural network models to extract vulnerability-indicative semantic features from the token sequence, attributed control flow graph (ACFG) and abstract syntax tree (AST) representations of a function, respectively. These features capture different perspectives of the code, which are then fused to enable S2FVD to accurately detect vulnerabilities that are well-hidden within a function. The experiments conducted on two large vulnerability datasets demonstrated the superior performance of S2FVD against state-of-the-art approaches, with its accuracy and F1 scores reaching 98.07% and 98.14% respectively in detecting the presence of vulnerabilities, and 97.93% and 97.94%, respectively, in pinpointing specific vulnerability types. Furthermore, with regard to the real-world dataset D2A, S2FVD achieved average performance gains of 6.86% and 14.84% in terms of accuracy and F1 metrics, respectively, over the state-of-the-art baselines. This ablation study also confirms the superiority of fusing the semantics implied in multiple distinct code views to further enhance vulnerability detection performance.

Dynamic meshes reasonably represent time-varying 3D objects, but compression is required due to the large amount of data involved. One efficient framework decomposes a dynamic mesh into a base mesh and displacements using decimation and subdivision. The displacements are converted to levels by wavelet transforms and quantization, and they are coded by arithmetic coding. The levels of the current frame are predicted from the reference frame, and only the residuals are coded. However, the residual tends to be large since the coefficients of each frame are quantized before performing inter prediction. In this paper, we propose a method of quantizing the residuals obtained after applying inter prediction in order to reduce the amount of required data. The experimental results show that the proposed method improves coding efficiency (BD-Rate: -0.3 %) and that the reconstructed mesh has no quality degradations.

In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh vertices into two sets and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or socalled descriptions are transmitted independently. The 3D dynamic mesh coder is based on predictive coding with spatial and temporal layered decomposition. In addition, the proposed method allows for different redundancy allocations by duplicating a number of encoded spatial layers in both sets. The algorithm is evaluated with redundancy-rate-distortion curves and flexible trade-off between redundancy and side distortions can be achieved.

Dynamic meshes reasonably represent time-varying 3D objects, but compression is required due to the large amount of data involved. One efficient framework decomposes a dynamic mesh into a base mesh and displacements using decimation and subdivision. The displacements are converted to levels by wavelet transforms and quantization, and they are coded by arithmetic coding. The levels of the current frame are predicted from the reference frame, and only the residuals are coded. However, quantization errors occur two times in the reference frame and the current frame since the coefficients of each frame are quantized before performing inter prediction. In this paper, we propose a method of quantizing the residuals obtained after applying inter prediction in order to reduce the amount of required data. The experimental results show that the proposed method yields improved coding efficiency and that the reconstructed mesh has no quality degradations.

In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh frames into two sets by temporal subsampling and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or so-called descriptions are transmitted independently. The 3D dynamic mesh coder is based on predictive coding with spatial and temporal layered decomposition. In addition, the proposed method allows for different redundancy allocations by including a number of encoded spatial layers of the frames in the other set. The algorithm is evaluated with redundancy-rate-distortion curves and it is shown that, when one of the descriptions is lost, acceptable quality can be achieved with around 50% redundancy.

It has long been claimed that the mitochondrial genetic code possesses more symmetries than the Standard Genetic Code (SGC). To test this claim, the symmetrical structure of the SGC is compared with noncanonical genetic codes. We analyzed the symmetries of the graphs of codons and their respective phenotypic graph representation spanned by the RNY (R purines, Y pyrimidines, and N any of them) code, two RNA Extended codes, the SGC, as well as three different mitochondrial genetic codes from yeast, invertebrates, and vertebrates. The symmetry groups of the SGC and their corresponding phenotypic graphs of amino acids expose the evolvability of the SGC. Indeed, the analyzed mitochondrial genetic codes are more symmetrical than the SGC.