High payload H.266/Versatile Video Coding (VVC) steganography based on affine motion estimation and chaotic maps

In physics-based simulation of rigid or nearly rigid objects, collisions often become the primary performance bottleneck, particularly when enforcing intersection-free constraints. Previous simulation frameworks rely on primitive-level CCD algorithms. Due to the large number of colliding surface primitives to process, those methods are computationally intensive and heavily dependent on advanced parallel computing resources such as GPUs, which are often inaccessible due to competing tasks or capped threading capacity in applications like policy training for robotics. To address these limitations, we propose a sequential CCD algorithm for convex shapes undergoing constant affine motion. This approach uses the conservative advancement method to iteratively refine a lower-bound estimate of the TOI, exploiting the linearity of affine motion and the efficiency of convex shape distance computation. Our CCD algorithm integrates seamlessly into the ABD framework, achieving a 10-fold speed-up over primitive-level CCD. Its high single-threaded efficiency further enables significant throughput improvements via scene-level parallelism, making it well-suited for resource-constrained environments.

Affine Motion Estimation Hardware Implementation With 51.7%/67.5% Internal Bandwidth Reduction for Versatile Video Coding

A Coding Efficiency-Aware Hardware Design for VVC Affine Motion Estimation Reconstructor

Hardware implementation of iterative method for enhanced affine motion estimation in Versatile video coding

Fast Linear Equation Solving Algorithm and its Pipelined Hardware Architecture Design for VVC Affine Motion Estimation

We present a dynamic light scattering setup to probe, with time and space resolution, the microscopic dynamics of soft matter systems confined within millimeter-sized spherical drops. By using an ad hoc optical layout, we tackle the challenges raised by refraction effects due to the unconventional shape of the samples. We first validate the setup by investigating the dynamics of a suspension of Brownian particles. The dynamics measured at different positions in the drop, and hence different scattering angles, are found to be in excellent agreement with those obtained for the same sample in a conventional light scattering setup. We then demonstrate the setup capabilities by investigating a bead made of a polymer hydrogel undergoing swelling. The gel microscopic dynamics exhibit a space dependence that strongly varies with time elapsed since the beginning of swelling. Initially, the dynamics in the periphery of the bead are much faster than in the core, indicative of nonuniform swelling. As the swelling proceeds, the dynamics slow down and become more spatially homogeneous. By comparing the experimental results to numerical and analytical calculations for the dynamics of a homogeneous, purely elastic sphere undergoing swelling, we establish that the mean square displacement of the gel strands deviates from the affine motion inferred from the macroscopic deformation, evolving from fast diffusivelike dynamics at the onset of swelling to slower, yet supradiffusive, rearrangements at later stages.

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

The mechanics of a foam depends on bubble shape, bubble network topology, and the material at hand, be it metallic or polymeric, for example. While the shapes of bubbles are the consequence of minimizing surface area for a given bubble volume in a space-filling packing, if one were to consider biological tissue as a foam-like material, the zoology of observed shapes of cells perhaps motivates different energetic contributions. Building on earlier two-dimensional results, here, we focus on a mean field approach to obtain the elastic moduli for an ordered, three-dimensional vertex model. We use the space-filling shape of a truncated octahedron and an energy functional containing a restoring surface area spring and a restoring volume spring. The tuning of the three-dimensional shape index exhibits a rigidity transition via a compatible–incompatible transition. Specifically, for smaller shape indices, both the target surface area and volume cannot be achieved, while beyond some critical value of the three-dimensional shape index, they can be, resulting in a zero-energy state. In addition to analytically determining the location of the transition in mean field, we find that the rigidity transition and the elastic moduli depend on the parameterization of the cell shape. This parameterization effect is more pronounced in three dimensions than in two dimensions given the zoology of shapes that a polyhedron can take on (as compared to a polygon). We also uncover nontrivial dependence of the elastic moduli on the deformation protocol in which some deformations result in affine motion of the vertices, while others result in nonaffine motion. Such dependencies on the shape parameterization and deformation protocol give rise to a nontrivial shape landscape and, therefore, nontrivial mechanical response even in the absence of topology changes.

Occupancy map-based low complexity motion prediction for video-based point cloud compression

In this article, we address the problem of estimating fluid flows between two adjacent images containing fluid and non-fluid objects. Typically, traditional optical flow estimation methods lack accuracy, because of the highly deformable nature of fluid, the lack of definitive features, and the motion differences between fluid and non-fluid objects. Our approach captures fluid motions using an affine motion model for each small patch of an image. To obtain robust patch matches, we propose a best-buddies similarity-based method to address the lack of definitive features but many similar features in fluid phenomena. A dense set of affine motion models was then obtained by performing nearest-neighbor interpolation. Finally, dense fluid flow was recovered by applying the affine transformation to each patch and was improved by minimizing a variational energy function. Our method was validated using different types of fluid images. Experimental results show that the proposed method achieves the best performance.

Granular media near jamming exhibit fascinating properties, which can be harnessed to create jammed-granulate metamaterials: materials whose characteristics arise not only from the shape and material properties of the particles at the microscale but also from the geometric features of the packing. For the case of a bending beam made from jammed-granulate metamaterial, we study the impact of the particles' properties on the metamaterial's macroscopic mechanical characteristics. We find that the metamaterial's stiffness emerges from its volume fraction, in turn originating from its creation protocol; its ultimate strength corresponds to yielding of the force network. In contrast to many traditional materials, we find that macroscopic deformation occurs mostly through affine motion within the packing, aided by stress relief through local plastic events, surprisingly homogeneously spread and persistent throughout bending. Published by the American Physical Society 2024

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel accuracy using an affine motion model to capture the camera platform motion. The frames are externally upsampled using single-image interpolation. The interpolated frames are then fused with the custom EWF layer, employing subpixel registration information to give more weight to pixels with less interpolation error. Realistic image acquisition conditions are simulated to generate training and testing datasets with corresponding ground truths. The observation model captures optical degradation from diffraction and detector integration from the sensor. The experimental results demonstrate the efficacy of EFIF-Net using both simulated and real camera data. The real camera results use authentic, unaltered camera data without artificial downsampling or degradation.

The affine prediction is the main novelty in the inter-frame prediction of Versatile Video Coding (VVC) standard. However, implementing the affine prediction requires a huge computational effort that makes mandatory the use of dedicated hardware accelerators to achieve real-time processing and meet the constraints of area and power dissipation for mobile devices. In light of this, this work presents different approaches for a hardware design dedicated to solving the linear equation system, which is essential for refiningthe affine Motion Vector. Synthesis results show that the High- Throughput architecture, which explores the parallelism of internal operations, can reach an accuracy of 99.99%, requiring 333.9kgates to be implemented and presenting a power dissipation of 120.4mW when running at 540MHz, the operational frequency required to process 60 frames per second of HD 1080p videos.

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

Multi-stage affine motion estimation fast algorithm for versatile video coding using decision tree

Influence of printing conditions on the extrudate shape and fiber orientation in extrusion deposition additive manufacturing

Abstract The validity of Korn’s first inequality in the fractional setting in bounded domains has been open. We resolve this problem by proving that in fact Korn’s first inequality holds in the case p s > 1 ps\gt 1 for fractional W 0 s , p ( Ω ) {W}_{0}^{s,p}\left(\Omega ) Sobolev fields in open and bounded C 1 {C}^{1} -regular domains Ω ⊂ R n \Omega \subset {{\mathbb{R}}}^{n} . Also, in the case p s < 1 ps\lt 1 , for any open bounded C 1 {C}^{1} domain Ω ⊂ R n \Omega \subset {{\mathbb{R}}}^{n} , we construct counterexamples to the inequality, i.e., Korn’s first inequality fails to hold in bounded domains. The proof of the inequality in the case p s > 1 ps\gt 1 follows a standard compactness approach adopted in the classical case, combined with a Hardy inequality, and a recently proven Korn second inequality by Mengesha and Scott [A Fractional Korn-type inequality for smooth domains and a regularity estimate for nonlinear nonlocal systems of equations, Commun. Math. Sci. 20 (2022), no. 2, 405–423]. The counterexamples constructed in the case p s < 1 ps\lt 1 are interpolations of a constant affine rigid motion inside the domain away from the boundary and of the zero field close to the boundary.

Inter prediction is a crucial part of hybrid video coding frameworks, and it is used to eliminate redundancy in adjacent frames and improve coding performance. During inter prediction, motion estimation is used to find the reference block that is most similar to the current block, and the following motion compensation is used to shift the reference block fractionally to obtain the prediction block. The closer the reference block is to the original block, the higher the coding efficiency is. To improve the quality of reference blocks, a quality enhancement network (RBENN) that is dedicated to reference blocks is proposed. The main body of the network consists of 10 residual modules, with two convolution layers for preprocessing and feature extraction. Each residual module consists of two convolutional layers, one ReLU activation, and a shortcut. The network uses the luma reference block as input before motion compensation, and the enhanced reference block is then filtered by the default fractional interpolation. Moreover, the proposed method can be used for both conventional motion compensation and affine motion compensation. Experimental results showed that RBENN could achieve a −1.35% BD rate on average under the low-delay P (LDP) configuration compared with the latest H.266/VVC.

In this paper, we study infinitesimal transformations of the tangent bundle of a common path space. The general path space is a genera­liza­tion space of the affine connectivity. By affine connectivity of the com­mon path space, we construct an affine connection on the tangent bundle. For the infinitesimal transformation of the tangent bundle, a system of in­va­riance equations for the constructed affine connectivity is compiled. This system is a system of second-order differential equations with res­pect to the components of the infinitesimal transformation. The main re­sults of the article are obtained by analyzing this system taking into account the properties of homogeneous functions. It is shown that the comp­lete lift of an infinitesimal transformation of base is an infinitesimal af­fine motion of a tangent bundle if and only if the infinitesimal transfor­ma­tion of base is an affine motion in the general path space. Necessary and sufficient conditions are found that the infinitesimal transformation of a tangent bundle generated by a vertical vector field leaves the affine connec­ti­vity of the tangent bundle invariant. Conditions are given that are neces­sa­ry and sufficient so that the infinitesimal transformation of a tangent bundle with affine connectivity that preserves layers is an affine motion.