Superior Compression Performance Research Articles

Continuous fiber-reinforced 2.5D hybrid lattice structures with superior compression performance via self-supporting suspension printing

This paper proposes an innovative class of two-and-a-half dimensional (2.5D) hybrid continuous fiber reinforced lattice structures (CFRLSs) that rationally combine distinct lattice designs to leverage the tensile strength of fibers for achieving superior compression performance. These hybrid structures are fabricated through a self-supporting suspension printing (SSSP) method, which enables the fabrication of suspension structures across substantial gaps through continuous fiber 3D printing (CF-3DP). The compression behavior of the proposed 2.5 D hybrid CFRLSs was optimized by focusing on two key variables in hybridizing the basic lattice along the build direction: composition ratio and distribution strategy. Finite element and analytical models were developed to elucidate their three failure mechanisms and related control strategies. Compared to the conventional single-type structure, i.e., honeycomb design, the proposed hybrid structures show a substantially higher compression performance, with improvements of up to 141.3 % and 330.1 % in specific strength and modulus, respectively, even at a lower density. This hybrid lattice design method based on SSSP opens up new horizons for engineering high-performance CFRLSs with superior compression performance by fully exploiting the design freedom offered by CF-3DP.

Image Compression Based on DWT Using EZW/SPIHT Encoders

this paper reviews recent research on image compression techniques utilizing the Embedded Zero-Tree Wavelet (EZW) and Set Partitioning in Hierarchical Trees (SPIHT) algorithms. The study addresses the question of which algorithm, EZW or SPIHT, provides superior compression performance for various image types. The research method involves a comprehensive analysis of published research papers that employed these algorithms for image compression. The findings indicate that SPIHT generally outperforms EZW in terms of compression ratio, computational efficiency, and image quality, making it a more versatile and effective choice for various image compression applications.

EFFICIENT VIDEO COMPRESSION USING HEVC AND NON-LINEAR CONVOLUTIONAL MOBILEVNET BASED RATE-DISTORTION OPTIMIZATION

With the surge in demand for high-quality video content over various platforms, efficient video compression techniques have become indispensable. High-Efficiency Video Coding (HEVC) has been a cornerstone, yet further enhancements are essential for optimal compression. Despite HEVC’s advancements, achieving optimal compression while maintaining video quality remains challenging. Additionally, existing methods often overlook the computational complexity, hindering real-time applications. We propose a novel approach integrating HEVC with Non-Linear Convolutional MobileNet (NLCM) for enhanced compression efficiency. Our method employs a rate-distortion optimization framework, leveraging the capabilities of both HEVC and NLCM to achieve superior compression performance. NLCM provides adaptive filtering, enhancing spatial and temporal correlations, while HEVC ensures high compression efficiency. Through experimentation on standard video datasets, our method demonstrates significant improvements over existing techniques. Compared to HEVC alone, our approach achieves up to 30% reduction in bitrate at equivalent perceptual quality levels. Moreover, computational complexity is reduced by 15%, enabling real- time applications without compromising performance. The proposed method exhibits competitive results across various resolutions and frame rates, making it versatile for diverse video compression scenarios.

Self-similar pulse compression in a tapered Pb-silicate photonic crystal fiber at 2 µm

We report a 2-µm all-fiber nonlinear pulse compressor based on a tapered Pb-silicate photonic crystal fiber (PCF), which is capable of achieving large compression with low pedestal energy. A tapered Pb-silicate photonic crystal fiber with increased nonlinear coefficients is proposed for achieving self-similar pulse compression (SSPC) at 2 µm. The dynamic evolution of the fundamental order soliton is numerically analyzed based on the designed tapered fiber. After pulse compression in a tapered fiber with a length of 2.2 m, an initial 1.76 ps pulse can be compressed to 88 fs, increasing the peak power from 4.4 to 86 W with a compression factor of 20 and a quality factor of 98%. The results reveal that exponential variation yields superior compression performance and provides a promising solution for generating high-power femtosecond pulses at 2 µm.

Polyaniline/Polydopamine-Regulated Nitrogen-Doped Graphene Aerogel with Well-developed Mesoporous Structure for Supercapacitor Electrode

Graphene-based materials have been widely studied in the field of supercapacitors. However, their electrochemical properties and applications are still restricted by the susceptibility of graphene-based materials to curling and agglomeration during production. In this study, a nitrogen-doped graphene aerogel (NGA) based on the regulation of polydopamine (PDA) and polyaniline (PANI) is proposed for the development of supercapacitors using industrial grade graphene oxide (GO, 2.67 m2/g) as raw material. Due to the rigidity of the PANI, agglomeration is prevented during the reduction of GO sheets. Meanwhile, the flexibility and adhesion of the PDA ensure a stable interaction between the graphene sheets and the PANI. NGAs feature a well-developed mesoporous structure and a high content of nitrogen, which enables the rapid transfer of ions inside them. As revealed by density functional theory (DFT) calculations, NGA heterojunction is characterized by high electronic conductivity, fast electron transfer, and low adsorption energy. For this reason, the supercapacitor obtained with NGA as an electrode produces an excellent capacitive performance, achieving a high area capacitance (123.6 mF·cm−2 at 0.5 mA·cm−2) and a superior long-cycle compression performance (80.7%, 5000 cycles). This study provides an effective solution for the development of supercapacitor electrodes using low-cost GO as raw materials.

Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection.

The task of community detection, which aims to partition a network into clusters of nodes to summarize its large-scale structure, has spawned the development of many competing algorithms with varying objectives. Some community detection methods are inferential, explicitly deriving the clustering objective through a probabilistic generative model, while other methods are descriptive, dividing a network according to an objective motivated by a particular application, making it challenging to compare these methods on the same scale. Here we present a solution to this problem that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model. This allows us to compute the description length of a network and its partition under arbitrary objectives, providing a principled measure to compare the performance of different algorithms without the need for "ground-truth" labels. Our approach also gives access to instances of the community detection problem that are optimal to any given algorithm and in this way reveals intrinsic biases in popular descriptive methods, explaining their tendency to overfit. Using our framework, we compare a number of community detection methods on artificial networks and on a corpus of over 500 structurally diverse empirical networks. We find that more expressive community detection methods exhibit consistently superior compression performance on structured data instances, without having degraded performance on a minority of situations where more specialized algorithms perform optimally. Our results undermine the implications of the "no free lunch" theorem for community detection, both conceptually and in practice, since it is confined to unstructured data instances, unlike relevant community detection problems which are structured by requirement.

Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks

Among various network compression methods, network pruning has developed rapidly due to its superior compression performance. However, the trivial pruning threshold limits the compression performance of pruning. Most conventional pruning threshold methods are based on well-known hard or soft techniques that rely on time-consuming handcrafted tests or domain experience. To mitigate these issues, we propose a simple yet effective general pruning threshold method from an optimization point of view. Specifically, the pruning threshold problem is formulated as a constrained optimization program that minimizes the size of each layer. More importantly, our pruning threshold method together with conventional pruning works achieves a better performance across various pruning scenarios on many advanced benchmarks. Notably, for the L1-norm pruning algorithm with VGG-16, our method achieves higher FLOPs reductions without utilizing time-consuming sensibility analysis. The compression ratio boosts from 34% to 53%, which is a huge improvement. Similar experiments with ResNet-56 reveal that, even for compact networks, our method achieves competitive compression performance even without skipping any sensitive layers.

Learning Bilateral Clipping Parametric Activation for Low-Bit Neural Networks

Among various network compression methods, network quantization has developed rapidly due to its superior compression performance. However, trivial activation quantization schemes limit the compression performance of network quantization. Most conventional activation quantization methods directly utilize the rectified activation functions to quantize models, yet their unbounded outputs generally yield drastic accuracy degradation. To tackle this problem, we propose a comprehensive activation quantization technique namely Bilateral Clipping Parametric Rectified Linear Unit (BCPReLU) as a generalized version of all rectified activation functions, which limits the quantization range more flexibly during training. Specifically, trainable slopes and thresholds are introduced for both positive and negative inputs to find more flexible quantization scales. We theoretically demonstrate that BCPReLU has approximately the same expressive power as the corresponding unbounded version and establish its convergence in low-bit quantization networks. Extensive experiments on a variety of datasets and network architectures demonstrate the effectiveness of our trainable clipping activation function.

Water-assisted synthesis of phenolic aerogel with superior compression and thermal insulation performance enabled by thick-united nano-structure

The applications of traditional organic aerogels are commonly restricted by their powder falling, expensive preparation, and poor mechanical property. Herein, we report a phenolic resin aerogel based on the nano-structure of thick-united phenolic particles instead of the traditional thin-united connection. The aerogels fabricated through sol–gel reaction and interfacial polymerization are endowed with superior mechanical and outstanding thermal insulation performances. The 3D networks of phenolic resin aerogel obtained through the water-assisted method (PRAW) with domain sizes below 90 nm result from the phase separation in nanoscale. The highly porous PRAW exhibits excellent compressive properties with the compressive strength and modulus reaching 18.3 and 62.23 MPa, respectively, when the strain exceeds 50% without catastrophic collapse. Furthermore, it possesses excellent high temperature protection ability: a 20 mm thick sample can resist the temperature of 1200 °C for 5 min, with a final back-side temperature of 57.7 °C. This newly inexpensive and environmental-friendly methodology for PRAW preparation provide a broad application prospect in the field of thermal insulation under harsh environments.

Attention-Based Bi-Prediction Network for Versatile Video Coding (VVC) over 5G Network

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively.

Synthesis of a novel Al foam with a periodic architecture by introducing hollow Al tubes and Al/Mg powders

In this work, we synthesized a brand-new Al foam with a periodic structure via a simple powder metallurgical route. The periodic architecture consists of both hierarchical porous and bi-directional composition-graded structures. The results show that the hierarchical porous material includes large pores on millimeter scale inheriting from the hollow structure of the Al tubes, and small pores on micrometer scale produced by the sintering of Al/Mg powders. The bi-directional Mg concentration-graded structure is formed in the tube walls due to the condensation of Mg vapor in the inner tube wall. The addition of Mg powders achieves excellent metallurgical bonding between the Al powders and the hollow tubes at 550°C. The plateau stress and energy absorption capacity of the Al foam in y-axis compression are significantly higher than that in the x-axis due to their anisotropic structure. In general, the Al foam with Mg addition presents the most superior compression performance, and we believe that our findings could open up a unique strategy for developing high-performance metallic foams with the periodic architecture involving both hierarchical porous and bi-directional graded structure.

PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher–student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets.

A novel hierarchical light field coding scheme based on hybrid stacked multiplicative layers and Fourier disparity layers for glasses-free 3D displays

We present a novel hierarchical coding scheme for light fields based on transmittance patterns of low-rank multiplicative layers and Fourier disparity layers. The proposed scheme identifies multiplicative layers of light field view subsets optimized using convolutional neural networks for different scanning orders. Our approach exploits the hidden low-rank structure in the multiplicative layers obtained from the subsets of different scanning patterns. The spatial redundancies in the multiplicative layers can be efficiently removed by performing low-rank approximation at different ranks on the Krylov subspace. The intra-view and inter-view redundancies between approximated layers are further removed by HEVC encoding. Next, a Fourier disparity layer representation is constructed from the first subset of the approximated light field based on the chosen hierarchical order. Subsequent view subsets are synthesized by modeling the Fourier disparity layers that iteratively refine the representation with improved accuracy. The critical advantage of the proposed hybrid layered representation and coding scheme is that it utilizes not just spatial and temporal redundancies in light fields, but also efficiently exploits intrinsic similarities among neighboring sub-aperture images in both horizontal and vertical directions as specified by different predication orders. In addition, the scheme is flexible to realize a range of multiple bitrates at the decoder within a single integrated system. Comparison with state-of-the-art light field coders exhibits superior compression performance of the proposed scheme for real-world light fields. We achieve substantial bitrate savings and also maintain good light field reconstruction quality.

Improving sign-algorithm convergence rate using natural gradient for lossless audio compression

In lossless audio compression, the predictive residuals must remain sparse when entropy coding is applied. The sign algorithm (SA) is a conventional method for minimizing the magnitudes of residuals; however, this approach yields poor convergence performance compared with the least mean square algorithm. To overcome this convergence performance degradation, we propose novel adaptive algorithms based on a natural gradient: the natural-gradient sign algorithm (NGSA) and normalized NGSA. We also propose an efficient natural-gradient update method based on the AR(p) model, which requires mathcal {O}(p) multiply–add operations at every adaptation step. In experiments conducted using toy and real music data, the proposed algorithms achieve superior convergence performance to the SA. Furthermore, we propose a novel lossless audio codec based on the NGSA, called the natural-gradient autoregressive unlossy audio compressor (NARU), which is open-source and implemented in C. In a comparative experiment with existing, well-known codecs, NARU exhibits superior compression performance. These results suggest that the proposed methods are appropriate for practical applications.

Iris Image Compression Using Deep Convolutional Neural Networks.

Compression is a way of encoding digital data so that it takes up less storage and requires less network bandwidth to be transmitted, which is currently an imperative need for iris recognition systems due to the large amounts of data involved, while deep neural networks trained as image auto-encoders have recently emerged a promising direction for advancing the state-of-the-art in image compression, yet the generalizability of these schemes to preserve the unique biometric traits has been questioned when utilized in the corresponding recognition systems. For the first time, we thoroughly investigate the compression effectiveness of DSSLIC, a deep-learning-based image compression model specifically well suited for iris data compression, along with an additional deep-learning based lossy image compression technique. In particular, we relate Full-Reference image quality as measured in terms of Multi-scale Structural Similarity Index (MS-SSIM) and Local Feature Based Visual Security (LFBVS), as well as No-Reference images quality as measured in terms of the Blind Reference-less Image Spatial Quality Evaluator (BRISQUE), to the recognition scores as obtained by a set of concrete recognition systems. We further compare the DSSLIC model performance against several state-of-the-art (non-learning-based) lossy image compression techniques including: the ISO standard JPEG2000, JPEG, H.265 derivate BPG, HEVC, VCC, and AV1 to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression and promising recognition performance of the model over all other techniques on different iris databases.

Multifunctional Superelastic, Superhydrophilic, and Ultralight Nanocellulose‐Based Composite Carbon Aerogels for Compressive Supercapacitor and Strain Sensor

AbstractDeveloping superelastic and superhydrophilic carbon aerogels with intriguing mechanical properties is urgently desired for achieving promising performances in highly compressive supercapacitors and strain sensors. Herein, based on synergistic hydrogen bonding, electrostatic interaction, and π–π interaction within regularly arranged layered porous structures, conductive carbon aerogels with cellulose nanofibrils (CNF), carbon nanotubes (CNT) and reduced graphene oxide (RGO) are developed via bidirectional freezing and subsequent annealing. Benefiting from the porous architecture and high surface roughness, CNF/CNT/RGO carbon aerogels exhibit ultralow density (2.64 mg cm–3) and superhydrophilicity (water contact angle ≈0° at 106 ms). The honeycomb‐like ordered porous structure can efficiently transfer stress in the entire microstructure, thereby endowing carbon aerogels with high compressibility and extraordinary fatigue resistance (10,000 cycles at 50% strain). These aerogels can be assembled into compressive solid‐state symmetric supercapacitors showing excellent area capacitance (109.4 mF cm–2 at 0.4 mA cm–2) and superior long cycle compression performance (88% after 5000 cycles at compressive strain of 50%). Furthermore, the aerogels reveal good linear sensitivity (S = 5.61 kPa–1) and accurately capture human bio‐signals as strain sensors. It is expected that such CNF/CNT/RGO carbon aerogels will provide a novel multifunctional platform for wearable electronics, electronic skin, and human motion monitoring.

Compression and flexural properties of rigid polyurethane foam composites reinforced with 3D-printed polylactic acid lattice structures

Lightweight rigid polyurethane foam (RPUF) composites with favorable mechanical properties are demanded by numerous engineering applications. The mechanical performance of foam composites could be customized and enhanced by embedding geometric skeletons. The advancement of 3D printing technology has enabled greater freedom in manufacturing geometrically complex products. In this paper, two types of lattice structures were 3D printed with fused filament fabrication (FFF). Structural composites were prepared by filling the lattices with RPUF through the free rising method. Experimental testing and FEA modeling demonstrated that the lattice structures had significant effects on the composites’ stress dissipation and fracture forms. Compared to neat RPUF, the composites exhibited greater elastic limit, compression modulus, energy absorption capabilities, flexural strength, and flexural modulus. Lattices with more struts and greater density resulted in superior compression and flexural performance. This study shows that FFF lattices are suitable for constructing RPUF composites with enhanced flexural and compression properties.

Performance evaluation of lossy quality compression algorithms for RNA-seq data

BackgroundRecent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear.ResultsIn this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels.ConclusionsLossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results.

Numerical simulation of static mechanical properties of PMMA microcellular foams

Inspired by recent advancements and owing to their low weight, flexible design, and acceptable cushioning and shock absorption, microcellular foams are being widely utilized in the automotive, helmet, aerospace, and transportation packaging fields. Herein, we research the relationship between the mechanical properties of microcellular foams and their meso-structure. This study combines static mechanical property tests with numerical simulations to analyze and predict the effects of different void porosities, cell sizes, and cell morphologies on the static compressive properties of polymethyl-methacrylate (PMMA) microcellular foams. It was determined that the void porosity of the foam had the most significant influence on the static compression performance. For foams with the same average cell size (7 ± 1 μm), the compressive strength increased by 144% (the void porosity was from 65% to 37%); for foams with the same void porosity (64 ± 1%), the compressive strength increased by 42% (the average cell size was from 21 μm to 8 μm). The cell morphology had the least influence on the static mechanical properties. The ellipsoidal cells had a superior compression performance compared to the spherical and the polyhedral cells; the compressive strength increased by 8.2%.

Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission

Compressed sensing is well known for its superior compression performance, in existing schemes, in lossy compression. Conventional research aims to reach a larger compression ratio at the encoder, with acceptable quality reconstructed images at the decoder. This implies looking for compression performance with error-free transmission between the encoder and the decoder. Besides looking at compression performance, we applied block compressed sensing to digital images for robust transmission. For transmission over lossy channels, error propagation or data loss can be expected, and protection mechanisms for compressed sensing signals are required for guaranteed quality of the reconstructed images. We propose transmitting compressed sensing signals over multiple independent channels for robust transmission. By introducing correlations with multiple-description coding, which is an effective means for error resilient coding, errors induced in the lossy channels can effectively be alleviated. Simulation results presented the applicability and superiority of performance, depicting the effectiveness of protection of compressed sensing signals.