Search

Cross-modal compression (CMC) aims to compress highly redundant visual data into compact, common, and human-comprehensible domains, such as text, to preserve semantic fidelity. However, CMC is limited by a constant level of semantic fidelity and constrained semantic fidelity due to a single compression domain (plain text). To address these issues, we propose a new approach called Multiple-domains rate-distortion optimized CMC (M-CMC). Specifically, our method divides the image into two complementary representations: 1) a structure representation with an edge map, and 2) a texture representation with dense captions, which include numerous region-caption pairs instead of plain text. In this way, we expand the single domain to multiple domains, namely, edge maps, regions, and text. To achieve diverse levels of semantic fidelity, we suggest a rate-distortion reward function, where the distortion measures the semantic fidelity of reconstructed images and the rate measures the information content of the text. We also propose Multiple-stage Self-Critical Sequence Training (MSCST) to optimize the reward function. Extensive experimental results demonstrate that the proposed method achieves diverse levels of semantic translation more effectively than other CMC-based methods, achieves higher semantic compression performance compared to traditional block-based and learning-based image compression frameworks with 97,000-500 times compression ratio, and provides a simple yet effective way for image editing.

To cost-effectively transmit high-quality dynamic 3D human images in immersive multimedia applications, efficient data compression is crucial. Unlike existing methods that focus on reducing signal-level reconstruction errors, we propose the first dynamic 3D human compression framework based on human priors. The layered coding architecture significantly enhances the perceptual quality while also supporting a variety of downstream tasks, including visual analysis and content editing. Specifically, a high-fidelity pose-driven Avatar is generated from the original frames as the basic structure layer to implicitly represent the human shape. Then, human movements between frames are parameterized via a commonly-used human prior model, i.e., the Skinned Multi-Person Linear Model (SMPL), to form the motion layer and drive the Avatar. Furthermore, the normals are also introduced as an enhancement layer to preserve fine-grained geometric details. Finally, the Avatar, SMPL parameters, and normal maps are efficiently compressed into layered semantic bitstreams. Extensive qualitative and quantitative experiments show that the proposed framework remarkably outperforms other state-of-the-art 3D codecs in terms of subjective quality with only a few bits. More notably, as the size or frame number of the 3D human sequence increases, the superiority of our framework in perceptual quality becomes more significant while saving more bitrates.

As a highly ill-posed issue, single-image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by the degradation procedure. According to the Nyquist sampling theory, the degradation leads to the aliasing effect and makes it hard to restore the correct textures from low-resolution (LR) images. In practice, there are correlations and self-similarities among the adjacent patches in the natural images. This article considers the self-similarity and proposes a hierarchical image super-resolution network (HSRNet) to suppress the influence of aliasing. We consider the SISR issue in the optimization perspective and propose an iterative solution pattern based on the half-quadratic splitting (HQS) method. To explore the texture with local image prior, we design a hierarchical exploration block (HEB) and progressive increase the receptive field. Furthermore, multilevel spatial attention (MSA) is devised to obtain the relations of adjacent feature and enhance the high-frequency information, which acts as a crucial role for visual experience. The experimental result shows that HSRNet achieves better quantitative and visual performance than other works and remits the aliasing more effectively.

Traditional neural network compression (NNC) methods decrease the model size and floating-point operations (FLOPs) in the manner of screening out unimportant weight parameters; however, the intrinsic sparsity characteristics have not been fully exploited. In this article, from the perspective of signal processing and analysis for network parameters, we propose to use a compressive sensing (CS)-based method, namely NNCS, for performance improvements. Our proposed NNCS is inspired by the discovery that sparsity levels of weight parameters in the transform domain are greater than those in the original domain. First, to achieve sparse representations for parameters in the transform domain during training, we incorporate a constrained CS model into loss function. Second, the proposed effective training process consists of two steps, where the first step trains raw weight parameters and induces and reconstructs their sparse representations and the second step trains transform coefficients to improve network performances. Finally, we transform the entire neural network into another new domain-based representation, and a sparser parameter distribution can be obtained to facilitate inference acceleration. Experimental results demonstrate that NNCS can significantly outperform the other existing state-of-the-art methods in terms of parameter reductions and FLOPs. With VGGNet on CIFAR-10, we decrease 94.8% parameters and achieve a 76.8% reduction of FLOPs, with 0.13% drop in Top-1 accuracy. With ResNet-50 on ImageNet, we decrease 75.6% parameters and achieve a 78.9% reduction of FLOPs, with 1.24% drop in Top-1 accuracy.

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for “end-to-end optimized neural video coding.” The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric.

Vehicles are no longer isolated entities in traffic environments, thanks to the development of IoV powered by 5G networks and their evolution into 6G. However, it is not enough for vehicles in a highly dynamic and complex traffic environment to make reliable and efficient decisions. As a result, this paper proposes a cloud-edge-end computing system with multi-streams for IoV, referred to as Vehicular Digital Retina (VDR). Local computing and edge computing are effectively integrated in the VDR system through the aid of vehicle-to-everything (V2X) networks, resulting in a heterogeneous computing environment that improves vehicles' perception and decision-making abilities with collaborative strategies. Once the system framework is presented, various important functions in the VDR system are explained in detail, including V2X-aided collaborative perception, V2X-aided stream sharing for collaborative learning, and V2X-aided secured collaboration. All of them enable the development of efficient mechanisms of data sharing and information interaction with high security for collaborative intelligent driving. We also present a case study with simulation results to demonstrate the effectiveness of the proposed VDR system.

The rate-distortion optimized quantization (RDOQ) provides significant coding gain in the third generation of Audio Video coding Standard (AVS3). However, the high computational complexity and strong data dependency in RDOQ impede the hardware implementation. To address these issues, we propose a zig-zag scanline-level parallelized RDOQ algorithm and its fully pipelined hardware architecture for AVS3 video coding. For algorithm optimization, we update the run-level context for rate estimation in the inner zig-zag scanline and propose an efficient RD cost calculation form in the optimal coefficient level (OCL) decision step. In the last significant coefficient (LSC) position decision step, a greedy strategy based algorithm is proposed to optimize the determination process in parallel. Moreover, the proposed parallelized RDOQ algorithm is accelerated by single instruction multiple data (SIMD) on the Intel X86 platform. For hardware architecture design, a fully pipelined hardware architecture is proposed with nine pipeline stages. This design can process multiple transform units in parallel when the height is less than 32. Experimental results show that the proposed algorithm achieves 31.37%, 28.58%, and 28.53% time-saving by 0.25%, 0.26%, and 0.27% Bjøntegaard delta rate (BD-Rate) increase on average under all intra (AI), random access (RA), and low delay B (LDB) configurations, respectively. The hardware implementation achieves 32 coefficients per cycle, and the area consumption is 1223.2-K logic gates when working at 471.2-MHz. It is proven that the proposed algorithm and hardware architecture design achieve a good trade-off between coding efficiency and hardware throughput.

In recent years, artificial intelligence-generated content (AIGC) enabled by foundation models has received increasing attention and is undergoing remarkable development. Text prompts can be elegantly translated/converted into high-quality, photo-realistic images. This remarkable feature, however, has introduced extremely high bandwidth requirements for compressing and transmitting the vast number of AI-generated images (AIGI) for such AIGC services. Despite this challenge, research on compression methods for AIGI is conspicuously lacking but undeniably necessary. This research addresses this critical gap by introducing the pioneering AIGI dataset, PKU-AIGI-500K, encompassing over 105k+ diverse prompts and 528k+ images derived from five major foundation models. Through this dataset, we delve into exploring and analyzing the essential characteristics of AIGC images and empirically prove that existing data-driven lossy compression methods achieve sub-optimal or less efficient rate-distortion performance without fine-tuning, primarily due to a domain shift between AIGIs and natural images. We comprehensively benchmark the rate-distortion performance and runtime complexity analysis of conventional and learned image coding solutions that are openly available, uncovering new insights for emerging studies in AIGI compression. Moreover, to harness the full potential of redundant information in AIGI and its corresponding text, we propose an AIGI compression model (Cross-Attention Transformer Codec, CATC) trained on this dataset as a strong baseline. Subsequent experimental results demonstrate that our proposed model achieves up to 30.09% bitrate reduction compared to the state-of-the-art (SOTA) H.266/VVC codec and outperforms the SOTA learned codec, paving the way for future research in AIGI compression.

Perceptual Lossless Compression (PLC) is a novel compression standard directed by the Audio Video coding Standard (AVS) work group. It defines a lightweight, low-latency, and visually lossless image compression framework, which offers an alternative mezzanine codec for most user-agnostic on-chip compression scenarios, alleviating the tension between growing transmission demands and expensive integration upgrades. In this paper, the technical designs in the development process of PLC will be fully introduced. The balance between feature modeling and ASIC implementation costs will be present throughout. A high throughput and low hardware complexity implementation will be detailed and evaluated namely HIM. Hopefully, the design of the PLC standard and HIM framework will bring new inspiration for the emerging low-latency interaction systems.

Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and different sizes in length and width. However, most of the existing studies pay less attention to crack data under different situations. Meanwhile, recent algorithms based on deep convolutional neural networks (DCNNs) have promoted the development of cutting-edge models for crack detection. Nevertheless, they usually focus on complex models for good performance, but ignore detection efficiency in practical applications. In this article, to address the first issue, we collected two new databases (i.e. Rain365 and Sun520) captured on rainy and sunny days respectively, which enrich the data of the open source community. For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet. Specifically, we introduce a novel olive-shaped structure for the encoder network, a lightweight multi-scale block and a new up-sampling method in the decoder network. Numerous experiments show that our model can better balance detection performance and efficiency compared with previous models. Especially, on the Sun520 dataset, our CarNet significantly advances the state-of-the-art performance with ODS F-score from 0.488 to 0.514. Meanwhile, it does so with an improved detection speed (104 frames per second) which is orders of magnitude faster than some recent DCNNs-based algorithms specially designed for crack detection.

Image quality assessment (IQA) is very important for both end-users and service providers since a high-quality image can significantly improve the user's quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this article, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content.

ABSTRACT In this performance-based study, the effect of palygorskite clays and carbon nanotubes (CNTs) on the rheological and hardened properties of a cement mortar and a blended mortar containing cement, [...]

Low-latency live video streaming proposes more challenges on designing Adaptive Bit Rate (ABR) algorithms than video-on-demand streaming. In this paper, we present Fleet, a solution that improves user quality of experience (QoE) in terms of video quality, video switches, video freezes, and latency. Fleet is designed to meet two operational challenges at the same time: 1) optimizing QoE under dynamic mobile network conditions and 2) ensuring a low latency experience with minimal visual quality degradation. The core of Fleet is a stochastic model predictive controller that incorporates network conditions and client states for bitrate adaptation. The idle period problem presents great challenges for bandwidth measurement and client state prediction. Fleet consists of an HTTP chunk level bandwidth measurement algorithm, and a practical live video streaming evolution model for client state prediction. Besides, a throughput probability predictor is trained to capture the mobile network’s uncertainty. And, a triple threshold playback speed controller is designed for latency management. Fleet is practically implemented in dash.js and evaluated over both synthetic and real-world mobile network conditions. Our study shows that compared with the state-of-the-art solution, Fleet achieves an average QoE improvement of 39.7% and 37.9%, respectively. Moreover, Fleet has a good generalization, outperforming existing algorithms even on new video traces and network conditions.

Intra prediction, which aims to remove the redundancies within a frame, has shown promising performance by simply projecting and interpolating samples along multiple angular directions. Recently, with numerous approaches devoted to learning nonlinear predictors with deep neural networks (DNN) based on local correlations, much less work has been dedicated to exploring non-local self-similarities in intra prediction. In this paper, we propose a unified prediction model that exploits both local and non-local correlations for intra prediction. The proposed model not only supports the nonlinear prediction using local reference samples as input, but also aggregates useful non-local information from a large reconstructed region with a Patch-level Non-local Attention Network (PNA-Net). More specifically, PNA-Net incorporates template matching with attention mechanism in feature domain to obtain the responses of all non-local features to the content to be predicted, leading to the prediction produced with weighted non-local patches. Finally, the predictions in the local and non-local manners are blended adaptively with a trainable network, ensuring the capability to handle a variety of contents. Experimental results on Versatile Video Coding (VVC) software VTM-11.0 show that the proposed model achieves on average 4.69% bit rate savings for natural scene sequences, and 4.24% bit rate savings for screen content sequences under the all intra configuration.

Inter prediction is the critical component in hybrid coding framework to deal with the temporal redundancy. Most of the neural video coding methods typically follow the motion compensation based inter coding scheme, establishing motion vector (MV) as the central role. In this paper, we innovatively propose an efficient motion modeling approach by inherently decomposing it into two components, the intrinsic motion and the compensatory motion. The intrinsic motion originates from the implicit spatiotemporal context hidden in the historical sequence, which can be intuitively captured free of bits. On the top of it, the compensatory motion acts a role of structural refinement and texture enhancement as a form of side information. In particular, the inter prediction is performed in the feature space as a manner of progressive temporal transition, conditioned on the decomposed motion. By the motion decomposition paradigm, we innovatively answer the question of motion representation, compensation and coding in the learned video compression framework. With the temporal prediction, the remaining pixel residue is signaled to obtain the reconstruction. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art coding performance on par with other end-to-end coding methods, and outperforms versatile video coding (VVC) under low-delay P (LDP) configuration in terms of MS-SSIM metric.

Fractional motion compensation (FMC) has been widely adopted in modern video coding standards such as high-efficiency video coding (HEVC). The fractional pixels in FMC are generated by the interpolation filters, and the coefficients of the filters are designed using a Fourier decomposition of the discrete cosine transform (DCT) in HEVC. To overcome the compression deficiency caused by these handcrafted filters, deep learning-based interpolation filter approaches have been introduced recently. In this paper, we analyze the drawbacks of the mean square error (MSE) only loss function which is widely adopted in existing learned-based approaches, and introduce the rate-distortion optimization (RDO) theory into network training to formulate a joint loss function to overcome this issue. Then, a new training scheme is proposed for the detailed implementation of the joint loss function. Besides the calculated MSE loss, the residual will be sent into a rate estimation model which includes three stages: transform/quantization/rate modeling, and the output rate cost will be used as the second part of the loss function. Finally, we propose a novel network structure that utilizes the deformable convolution layer and the residual dense block to enhance the feature extraction and transmission ability to adapt to complex video contents. The proposed RDO-based interpolation filter (RDOIF) has been integrated into HEVC reference software HM-16.7, and extensive experiments are conducted to verify the efficiency of the proposed method. Experimental results show that the proposed RDOIF method achieves 3.1%, 2.4%, and 1.4% BD-rate reduction on average under the low-delay-P, low-delay-B, and random access configurations, respectively. When implemented in the versatile video coding (VVC) test model VTM-10.0, the proposed method can achieve 2.3%, 1.2%, and 0.5% BD-rate savings on average under the low-delay-P, low-delay-B, and random access configurations, respectively.

Recently, remarkable progress has been made in learned image compression (LIC), in which nonlinear transforms (NTs) play a crucial role. Although there are many NT methods for improving the rate distortion performance, all the existing methods sacrifice the computational complexity and the number of parameters of the transformation. This paper provides a fundamental novel viewpoint on nonlinear transforms from a communication perspective, and shows how this idea can be extended to design efficient NT methods. In particular, the nonlinear transforms are inferred as signal modulation modules. Under this extrapolation, the current NTs are generalized as amplitude modulation that only varies the amplitude of the carrier wave. Therefore, a nonlinear modulation-like transform (NMLT) which varies the phase angle of the carrier is proposed. Moreover, this concept is extended by introducing In-phase/Quadrature (IQ) modulation, which is a boosting technique in communication field, in order to enhance NMLT. Furthermore, the Bit-interleaved technique in communication is used to guide the optimization of NTML with IQ. The experimental results on different datasets and backbone architectures verify the efficiency and robustness of the proposed methods. For example, when backbone architecture is hyperprior model, our method achieves 19.37% BD-rate reduction over GDN on the Kodak dataset. In addition, our method with channel wise autoregressive model leads to the state-of-the-art rate-distortion performance.

A new general synthesis of pharmaceutically important azolo[1,5-a]pyrimidines starting from widely available 3(5)-aminoazoles, aldehydes, and triethylamine is developed. The key is to enable the vinylation reaction that allows the in situ generation of elusive acyclic enamines and the subsequent annulation reaction to occur. This direct and practical strategy is capable of constructing a range of 5,6-unsubstituted pyrazolo[1,5-a]pyrimidines and [1,2,4]triazolo[1,5-a]pyrimidines. More importantly, this protocol provides a concise synthetic route to prepare the clinically used zaleplon.

Adaptive signal control is a method that dynamically adjusts signal phases based on real-time traffic conditions, aiming to improve the efficiency of intersection light control. However, in practical applications, obtaining complete traffic spatiotemporal states at intersections is challenging given the limited data sources. To address this issue, this paper presents a novel deep reinforcement learning model, namely 3DQN-PSTER, which combines Dueling Deep Q Network, Double Deep Q Network, and Priority SumTree Experience Replay technology. This model effectively extracts traffic states from sampled floating car data (FCD) and achieves optimal timing schemes. To evaluate its performance, the proposed model is tested in a simulated intersection environment using actual intersection data. The model interacts with the environment by adjusting signal phases, updating traffic states, and obtaining optimal timing schemes. Simulation results demonstrate that the proposed model exhibits higher convergence efficiency and outperforms benchmark models. When applied, the reinforcement learning single control (RLSC) based on the 3DQN-PSTER model outperforms fixed-time signal control schemes and actuated signal control (ASC) schemes. Even in scenarios with low penetration ratios of FCD, RLSC still significantly outperforms ASC. Therefore, the signal timing optimization method proposed in this paper could greatly enhance intersection operation efficiency in both ideal overall data environments and partial states.

Stereochemically pure saccharides have indispensable roles in fields ranging from medicinal chemistry to materials science and organic synthesis. However, the development of a simple, stereoselective, and efficient glycosylation protocol to access α- and β-C-glycosides (particularly 2-deoxy entities) remains a persistent challenge. Existing studies have primarily focused on C1 modification of carbohydrates and transformation of glycosyl radical precursors. Here, we innovate by harnessing the in situ generated glycosyl-Ni species to achieve one-pot borylation and glycosylation in a cascade manner, which is enabled by an earth-abundant nickel-catalyzed carboboration of readily accessible glycals without any ligand. This work reveals the potential for the development of a modular and multifunctional glycosylation platform to facilitate the simultaneous introduction of C–C and C–B bonds at the stereogenic center of saccharides, a largely unexploited research area. Preliminary experimental and computational studies indicate that the endocyclic O and the C3 group play important roles in stereoseclectively forging glycosidic bonds. As a result, a diverse range of C–R (R = alkyl, aryl, and alkenyl) and 2-deoxygenated glycosides bearing modifiable boron groups could be rapidly made with excellent stereocontrol and exhibit remarkable functional group tolerance. The synthetic potential is underscored in the late-stage glycosylation of natural products and commercial drugs as well as the facile preparation of various rare sugars, bioactive conjugates, and key intermediates to prorocentin, phomonol, and aspergillide A.

Light field (LF) representations aim to provide photo-realistic, free-viewpoint viewing experiences. However, the most popular LF representations are the images from multiple views. Multi-view image-based representations generally need to restrict the range or degrees of freedom of the viewing experience to what can be interpolated in the image domain, essentially because they lack explicit geometry information. We present a new surface LF (SLF) representation based on explicit geometry and a method for SLF compression. First, we map the multi-view images of a scene onto a 3-D geometric point cloud. The color of each point in the point cloud is a function of viewing direction known as a view map. We represent each view map efficiently in a B-Spline wavelet basis. This representation is capable of modeling diverse surface materials and complex lighting conditions in a highly scalable and adaptive manner. The coefficients of the B-Spline wavelet representation are then compressed spatially. To increase the spatial correlation and, thus, improve compression efficiency, we introduce a smoothing term to make the coefficients more similar across the 3-D space. We compress the coefficients spatially using existing point cloud compression methods. On the decoder side, the scene is rendered efficiently from any viewing direction by reconstructing the view map at each point. In contrast to multi-view image-based LF approaches, our method supports photo-realistic rendering of real-world scenes from arbitrary viewpoints, i.e., with an unlimited six degrees of freedom. In terms of rate and distortion, experimental results show that our method achieves superior performance with lighter decoder complexity compared with a reference image-plus-geometry compression scheme, indicating its potential in practical virtual and augmented reality applications.

Light field (LF) has become an attractive representation of immersive multimedia content for simultaneously capturing both the spatial and angular information of the light rays. In this paper, we present a LF image compression framework driven by a generative adversarial network (GAN)-based sub-aperture image (SAI) generation and a cascaded hierarchical coding structure. Specifically, we sparsely sample the SAIs in LF and propose the GAN of LF (LF-GAN) to generate the unsampled SAIs by analogy with adversarial learning conditioned on its surrounding contexts. In particular, the LF-GAN learns to interpret both the angular and spatial context of the LF structure and, meanwhile, generates intermediate hypothesis for the unsampled SAIs in a certain position. Subsequently, the sampled SAIs and the residues of the generated-unsampled SAIs are re-organized as pseudo-sequences and compressed by standard video codecs. Finally, the hierarchical coding structure is adopted for the sampled SAI to effectively remove the inter-view redundancies. During the training process of LF-GAN, the pixel-wise Euclidean loss and the adversarial loss are chosen as the optimization objective, such that sharp textures with less blurring in details can be produced. Extensive experimental results show that the proposed LF-GAN-based LF image compression framework outperforms the state-of-the-art learning-based LF image compression approach with on average 4.9% BD-rate reductions over multiple LF datasets.

Panoramic video is produced and displayed as if it is supported on sphere, but it is usually mapped to two-dimensional (2D) plane for compression purpose. The mapping from sphere to 2D plane inevitably causes deformation of video content, which shall be taken into account in panoramic video coding. In this paper, we propose a new motion model based on spherical coordinates transform to explicitly address the deformation problem. Different from the traditional 2D translational motion model, our model assumes a 3D translational motion, which is characterized by a 2D motion vector and a relative depth parameter. Based on spherical coordinates transform, we can deduce the motion compensation and motion estimation algorithms for our proposed motion model. We then integrate the algorithms into the joint exploration model (JEM) scheme for panoramic video coding, and perform experiments on some typical equirectangular projection format video sequences. Experimental results show that our proposed method leads to up to 10.7% and on average 2.6% BD-rate reduction compared to the JEM anchor on our test sequences, with no increase of decoding time.

Hybrid electric vehicles offer potential for reducing the oil consumption and the generation of greenhouse gases, but they have a high battery price and short battery longevity. This paper presents an energy management strategy for power-split hybrid electric vehicles, which seeks not only to reduce the gasoline consumption but also to prolong the battery life. The instantaneous battery usage is penalized by its influence on the battery lifespan. This influence is defined by a new concept of battery ageing, i.e. the battery-fading index, which represents the ageing rate of the battery in various conditions. The multi-objective optimization problem is achieved by a model predictive control framework. The results indicate that the proposed energy management strategy effectively reduces battery fading with only a relatively small increase in the fuel consumption.

Objectively accessing the quality of screen content images (SCIs) is a challenging problem, as SCIs may not always have identical properties as natural scenes. Here we conduct comprehensive studies on the subjective and objective quality assessment of the compressed SCIs. Firstly, we build a database that contains the distorted SCIs generated by the high efficiency video coding standard as well as its extension on screen content compression. Subsequently, subjective experiments are conducted to evaluate the perceived quality of these SCIs with compression artifacts. To automatically predict the subjective quality, a reduced-reference quality assessment model is further learnt by a set of wavelet domain features concerning the generalized spectral behavior, the fluctuations of the energy, and the information content with relatively large scale training samples. Our experimental results show that the learnt model is able to achieve better prediction on the SCI quality with a few extracted meaningful features.

Ever since the founding of the Audio and Video Coding Standard (AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two decades. The series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons, in particular in terms of the 8K ultra-high-definition (UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone, and live streaming solution for sports. In addition, the China Central Television (CCTV) of China Media Group (CMG), the state television of China, has officially launched the first 8K broadcasting channel (CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

Shape memory polymers (SMPs) based on natural rubber were fabricated by crosslinking epoxidized natural rubber with zinc diacrylate (ZDA) using the oxa-Michael reaction. These SMPs possessed excellent shape fixity and recovery. The glass transition largely accounted for the fixing of the SMPs temporary shape. Increasing the ZDA content allowed the trigger temperature (20–46 °C) and recovery time (14–33 s) of the SMPs to be continuously tuned. Nanosized silica (nanosilica) was incorporated into the neat polymers to further increase the flexibility and tune the recovery stress. The nanosilica–SMPs exhibited exceptionally high strength in a rubbery state (>20 MPa). The nanosilica–SMPs exhibited high transparency, making them suitable in visible heat-shrinkable tubes.

Compressive sensing (CS) has drawn quite an amount of attention as a joint sampling and compression approach. Its theory shows that when the signal is sparse enough in some domain, it can be decoded from many fewer measurements than suggested by the Nyquist sampling theory. So one of the most challenging researches in CS is to seek a domain where a signal can exhibit a high degree of sparsity and hence be recovered faithfully. Most of the conventional CS recovery approaches, however, exploited a set of fixed bases (e.g., DCT, wavelet, and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via collaborative sparsity, which enforces local 2-D sparsity and nonlocal 3-D sparsity simultaneously in an adaptive hybrid space-transform domain, thus substantially utilizing intrinsic sparsity of natural images and greatly confining the CS solution space. In addition, an efficient augmented Lagrangian-based technique is developed to solve the above optimization problem. Experimental results on a wide range of natural images are presented to demonstrate the efficacy of the new CS recovery strategy.

•Boundary nucleation and growth model for MgO hydration under ambient conditions.•No detectable carbonation of reactive MgO from brine under ambient conditions.•CO2curing of reactive MgO from brine leads to hydrated carbonates, strength gain.•Sodium bicarbonate and sodium sesquicarbonate in MgO enhances compressive strength.•Amorphous phase in MgO with NaHCO3and Na3H(CO3)2∙2H2O plays key strengthening role.

A flexible strain sensor based on a unique ZnO microwire network structure was constructed in this work. The current characteristics of the sensor were investigated by applying tensile and compressive static strains, and the sensor showed quick current response properties. Moreover, the sensor exhibited excellent response properties to the periodic microstrain with 1.0 and 0.2 Hz frequencies. The unique piezoelectric and piezoresistance of ZnO resulted in the sensor had excellent sensing performance to microstrains. The highest gauge factor of the sensor was up to 900. The results provide a new design method for high-performance microstrain sensors based on piezoelectric materials.