arXiv subjects

Xiaoqing Zhu

Publications and source records attributed to Xiaoqing Zhu.

At least 19 recordsLinked to original sources

arXiv2026

Transform and Entropy Coding in AV2

AV2 is the successor to the AV1 video coding standard developed by the Alliance for Open Media (AOMedia). Its primary objective is to deliver substantial compression gains and subjective quality improvements while maintaining low-complexity encoder and decoder operations. This paper describes the transform, quantization and entropy coding design in AV2, including redesigned transform kernels and data-driven transforms, expanded transform partitioning, and a mode & coefficient dependent transform signaling. AV2 introduces several new coding tools including Intra/Inter Secondary Transforms (IST), Trellis Coded Quantization (TCQ), Adaptive Transform Coding (ATC), Probability Adaptation Rate Adjustment (PARA), Forward Skip Coding (FSC), Cross Chroma Component Transforms (CCTX), Parity Hiding (PH) tools and improved lossless coding. These advances enable AV2 to deliver the highest quality video experience for video applications at a significantly reduced bitrate.

eess.IV↗

arXiv2025

Entropy-Controlled Intrinsic Motivation Reinforcement Learning for Quadruped Robot Locomotion in Complex Terrains

Learning is the basis of both biological and artificial systems when it comes to mimicking intelligent behaviors. From the classical PPO (Proximal Policy Optimization), there is a series of deep reinforcement learning algorithms which are widely used in training locomotion policies for quadrupedal robots because of their stability and sample efficiency. However, among all these variants, experiments and simulations often converge prematurely, leading to suboptimal locomotion and reduced task performance. Therefore, in this paper, we introduce Entropy-Controlled Intrinsic Motivation (ECIM), an entropy-based reinforcement learning algorithm in contrast with the PPO series, that can reduce premature convergence by combining intrinsic motivation with adaptive exploration. For experiments, in order to parallel with other baselines, we chose to apply it in Isaac Gym across six terrain categories: upward slopes, downward slopes, uneven rough terrain, ascending stairs, descending stairs, and flat ground as widely used. For comparison, our experiments consistently achieve better performance: task rewards increase by 4--12%, peak body pitch oscillation is reduced by 23--29%, joint acceleration decreases by 20--32%, and joint torque consumption declines by 11--20%. Overall, our model ECIM, by combining entropy control and intrinsic motivation control, achieves better results in stability across different terrains for quadrupedal locomotion, and at the same time reduces energetic cost and makes it a practical choice for complex robotic control tasks.

cs.RO↗

arXiv2025

C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales

In recent years, attention mechanisms have been exploited in single image super-resolution (SISR), achieving impressive reconstruction results. However, these advancements are still limited by the reliance on simple training strategies and network architectures designed for discrete up-sampling scales, which hinder the model's ability to effectively capture information across multiple scales. To address these limitations, we propose a novel framework, \textbf{C2D-ISR}, for optimizing attention-based image super-resolution models from both performance and complexity perspectives. Our approach is based on a two-stage training methodology and a hierarchical encoding mechanism. The new training methodology involves continuous-scale training for discrete scale models, enabling the learning of inter-scale correlations and multi-scale feature representation. In addition, we generalize the hierarchical encoding mechanism with existing attention-based network structures, which can achieve improved spatial feature fusion, cross-scale information aggregation, and more importantly, much faster inference. We have evaluated the C2D-ISR framework based on three efficient attention-based backbones, SwinIR-L, SRFormer-L and MambaIRv2-L, and demonstrated significant improvements over the other existing optimization framework, HiT, in terms of super-resolution performance (up to 0.2dB) and computational complexity reduction (up to 11%). The source code will be made publicly available at www.github.com.

cs.CV↗

arXiv2024

HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution

Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR). INR-based ISR methods typically learn continuous representations, providing flexibility for generating high-resolution images at any desired scale from their low-resolution counterparts. However, existing INR-based ISR methods utilize multi-layer perceptrons for parameterization in the network; this does not take account of the hierarchical structure existing in local sampling points and hence constrains the representation capability. In this paper, we propose a new \textbf{H}ierarchical encoding based \textbf{I}mplicit \textbf{I}mage \textbf{F}unction for continuous image super-resolution, \textbf{HIIF}, which leverages a novel hierarchical positional encoding that enhances the local implicit representation, enabling it to capture fine details at multiple scales. Our approach also embeds a multi-head linear attention mechanism within the implicit attention network by taking additional non-local information into account. Our experiments show that, when integrated with different backbone encoders, HIIF outperforms the state-of-the-art continuous image super-resolution methods by up to 0.17dB in PSNR. The source code of HIIF will be made publicly available at \url{www.github.com}.

cs.CV↗

arXiv2024

RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content

Super-resolution (SR) is a key technique for improving the visual quality of video content by increasing its spatial resolution while reconstructing fine details. SR has been employed in many applications including video streaming, where compressed low-resolution content is typically transmitted to end users and then reconstructed with a higher resolution and enhanced quality. To support real-time playback, it is important to implement fast SR models while preserving reconstruction quality; however most existing solutions, in particular those based on complex deep neural networks, fail to do so. To address this issue, this paper proposes a low-complexity SR method, RTSR, designed to enhance the visual quality of compressed video content, focusing on resolution up-scaling from a) 360p to 1080p and from b) 540p to 4K. The proposed approach utilizes a CNN-based network architecture, which was optimized for AV1 (SVT)-encoded content at various quantization levels based on a dual-teacher knowledge distillation method. This method was submitted to the AIM 2024 Video Super-Resolution Challenge, specifically targeting the Efficient/Mobile Real-Time Video Super-Resolution competition. It achieved the best trade-off between complexity and coding performance (measured in PSNR, SSIM and VMAF) among all six submissions. The code will be available soon.

eess.IV↗

arXiv2024

BVI-AOM: A New Training Dataset for Deep Video Compression Optimization

Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and representative training material for optimization in order to achieve model generalization and optimal coding performance. However, existing datasets either offer limited content variability or come with restricted licensing terms constraining their use to research purposes only. To address these issues, we propose a new training dataset, named BVI-AOM, which contains 956 uncompressed sequences at various resolutions from 270p to 2160p, covering a wide range of content and texture types. The dataset comes with more flexible licensing terms and offers competitive performance when used as a training set for optimizing deep video coding tools. The experimental results demonstrate that when used as a training set to optimize two popular network architectures for two different coding tools, the proposed dataset leads to additional bitrate savings of up to 0.29 and 2.98 percentage points in terms of PSNR-Y and VMAF, respectively, compared to an existing training dataset, BVI-DVC, which has been widely used for deep video coding. The BVI-AOM dataset is available at https://github.com/fan-aaron-zhang/bvi-aom

eess.IV↗

arXiv2022

Optimal Streaming Erasure Codes over the Three-Node Relay Network

This paper investigates low-latency streaming codes for a three-node relay network. The source transmits a sequence of messages (streaming messages) to the destination through the relay between them, where the first-hop channel from the source to the relay and the second-hop channel from the relay to the destination are subject to packet erasures. Every source message must be recovered perfectly at the destination subject to a fixed decoding delay of $T$ time slots. In any sliding window of $T+1$ time slots, we assume no more than $N_1$ and $N_2$ erasures are introduced by the first-hop channel and second-hop channel respectively. Under this channel loss assumption, we fully characterize the maximum achievable rate in terms of $T$, $N_1$ and $N_2$. The achievability is proved by using a symbol-wise decode-forward strategy where the source symbols within the same message are decoded by the relay with different delays. The converse is proved by analyzing the maximum achievable rate for each channel when the erasures in the other channel are consecutive (bursty). In addition, we show that traditional message-wise decode-forward strategies, which require the source symbols within the same message to be decoded by the relay with the same delay, are sub-optimal in general.

cs.IT↗

arXiv2022

Improving Maximum Likelihood Difference Scaling method to measure inter content scale

The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on a scale. However, procedures such as Maximum Likelihood Difference Scaling (MLDS) have shown that considering perceptual distances can bring benefits in terms of discriminatory power, observers' cognitive load, and the number of trials required. One of the disadvantages of the MLDS method is that the perceptual scales obtained for stimuli created from different source content are generally not comparable. In this paper, we propose an extension of the MLDS method that ensures inter-content comparability of the results and shows its usefulness especially in the presence of observer errors.

q-bio.NC↗

arXiv2021

Mechanisms of Molecular Ferroelectrics Made Simple

Molecular ferroelectrics have captured immense attention due to their superiority over inorganic oxide ferroelectrics, such as environmentally friendly, low-cost, flexible, foldable. However, the mechanisms of ferroelectric switching and phase transition for the molecular ferroelectrics are still missing, leaving the development of novel molecular ferroelectrics less efficient. In this work, we have provided a methodology combining molecular dynamics (MD) simulation on a polarized force field named polarized crystal charge (PCC) and enhanced sampling technique, replica-exchange molecular dynamics (REMD) to simulate such mechanisms. With this procedure, we have investigated a promising molecular ferroelectric material, (R)/(S)-3-quinuclidinol crystal. We have simulated the ferroelectric hysteresis loops of both enantiomers and obtained spontaneous polarization of 7/8 μC cm-2 and a corresponding coercive electric field of 15 kV cm-1. We also find the Curie temperature as 380/385 K for ferro-/para-electric phase transition of both enantiomers. All of the simulated results are highly compatible with experimental values. Besides of that, we predict a novel Curie temperature of about 600 K. This finding is further validated by principal component analysis (PCA). Our work would significantly promote the future exploration of multifunctional molecular ferroelectrics for the next generation of intelligent devices.

physics.chem-ph↗

arXiv2021

Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.

eess.SP↗

arXiv2020

Traffic Characteristics of Virtual Reality over Edge-enabled Wi-Fi Networks

Virtual reality (VR) is becoming prevalent with a plethora of applications in education, healthcare, entertainment, etc. To increase the user mobility, and to reduce the energy consumption and production cost of VR head mounted displays (HMDs), wireless VR with edge-computing has been the focus of both industry and academia. However, transferring large video frames of VR applications with their stringent Quality of Service (QoS) requirements over wireless network requires innovations and optimizations across different network layers. In order to develop efficient architectures, protocols and scheduling mechanisms, the traffic characteristics of various types of VR applications are required. In this paper, we first compute the theoretical throughput requirements of an ideal VR experience as well as a popular VR HMD. We then examine the traffic characteristics of a set of VR applications using an edge-enabled Wi-Fi network. Our results reveal interesting findings that can be considered in developing new optimizations, protocols, access mechanisms and scheduling algorithms.

cs.NI↗

arXiv2020

DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network's architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.

eess.SP↗

arXiv2020

Low-Latency Network-Adaptive Error Control for Interactive Streaming

We introduce a novel network-adaptive algorithm that is suitable for alleviating network packet losses for low-latency interactive communications between a source and a destination. Our network-adaptive algorithm estimates in real-time the best parameters of a recently proposed streaming code that uses forward error correction (FEC) to correct both arbitrary and burst losses, which cause a crackling noise and undesirable jitters, respectively in audio. In particular, the destination estimates appropriate coding parameters based on its observed packet loss pattern and sends them back to the source for updating the underlying code. Besides, a new explicit construction of practical low-latency streaming codes that achieve the optimal tradeoff between the capability of correcting arbitrary losses and the capability of correcting burst losses is provided. Simulation evaluations based on statistical losses and real-world packet loss traces reveal the following: (i) Our proposed network-adaptive algorithm combined with our optimal streaming codes can achieve significantly higher performance compared to uncoded and non-adaptive FEC schemes over UDP (User Datagram Protocol); (ii) Our explicit streaming codes can significantly outperform traditional MDS (maximum-distance separable) streaming schemes when they are used along with our network-adaptive algorithm.

cs.NI↗

arXiv2020

Streaming Erasure Codes over Multi-hop Relay Network

This paper studies low-latency streaming codes for the multi-hop network. The source is transmitting a sequence of messages (streaming messages) to a destination through a chain of relays where each hop is subject to packet erasures. Every source message has to be recovered perfectly at the destination within a delay constraint of $T$ time slots. In any sliding window of $T+1$ time slots, we assume no more than $N_j$ erasures introduced by the $j$'th hop channel. The capacity in case of a single relay (a three-node network) was derived by Fong [1], et al. While the converse derived for the three-node case can be extended to any number of nodes using a similar technique (analyzing the case where erasures on other links are consecutive), we demonstrate next that the achievable scheme, which suggested a clever symbol-wise decode and forward strategy, can not be straightforwardly extended without a loss in performance. The coding scheme for the three-node network, which was shown to achieve the upper bound, was ``state-independent'' (i.e., it does not depend on specific erasure pattern). While this is a very desirable property, in this paper, we suggest a ``state-dependent'' (i.e., a scheme which depends on specific erasure pattern) and show that it achieves the upper bound up to the size of an additional header. Since, as we show, the size of the header does not depend on the field size, the gap between the achievable rate and the upper bound decreases as the field size increases.

cs.IT↗

arXiv2020

Terahertz emission in the van der Waals magnet CrSiTe3

The van der Waals magnet CrSiTe3 (CST) has captured immense interest because it is capable of retaining the long-range ferromagnetic order even in its monolayer form, thus offering potential use in spintronic devices. Bulk CST crystal has inversion symmetry that is broken on the crystal surface. Here, by employing ultrafast terahertz (THz) emission spectroscopy and time resolved THz spectroscopy, the THz emission of the CST crystal was investigated, which shows a strong THz emission from the crystal surface under femtosecond (fs) pulse excitation at 800 nm. Theoretical analysis based on space symmetry of CST suggests the dominant role of shift current occurring on the surface with a thickness of a few quintuple layers in producing the THz emission, in consistence with the experimental observation that the emitted THz amplitude strongly depends on the azimuthal and pumping polarization angles. The present study offers a new efficient THz emitter as well as a better understanding of the nonlinear optical response of CST. It hopefully will open a window toward the investigation on the nonlinear optical response in the mono-/few-layer van der Waals crystals with low-dimensional magnetism.

physics.optics↗

arXiv2019

Optimal Multiplexed Erasure Codes for Streaming Messages with Different Decoding Delays

This paper considers multiplexing two sequences of messages with two different decoding delays over a packet erasure channel. In each time slot, the source constructs a packet based on the current and previous messages and transmits the packet, which may be erased when the packet travels from the source to the destination. The destination must perfectly recover every source message in the first sequence subject to a decoding delay $T_\mathrm{v}$ and every source message in the second sequence subject to a shorter decoding delay $T_\mathrm{u}\le T_\mathrm{v}$. We assume that the channel loss model introduces a burst erasure of a fixed length $B$ on the discrete timeline. Under this channel loss assumption, the capacity region for the case where $T_\mathrm{v}\le T_\mathrm{u}+B$ was previously solved. In this paper, we fully characterize the capacity region for the remaining case $T_\mathrm{v}> T_\mathrm{u}+B$. The key step in the achievability proof is achieving the non-trivial corner point of the capacity region through using a multiplexed streaming code constructed by superimposing two single-stream codes. The main idea in the converse proof is obtaining a genie-aided bound when the channel is subject to a periodic erasure pattern where each period consists of a length-$B$ burst erasure followed by a length-$T_\mathrm{u}$ noiseless duration.

cs.IT↗

arXiv2018

Optimal Streaming Codes for Channels with Burst and Arbitrary Erasures

This paper considers transmitting a sequence of messages (a streaming source) over a packet erasure channel. In each time slot, the source constructs a packet based on the current and the previous messages and transmits the packet, which may be erased when the packet travels from the source to the destination. Every source message must be recovered perfectly at the destination subject to a fixed decoding delay. We assume that the channel loss model introduces either one burst erasure or multiple arbitrary erasures in any fixed-sized sliding window. Under this channel loss assumption, we fully characterize the maximum achievable rate by constructing streaming codes that achieve the optimal rate. In addition, our construction of optimal streaming codes implies the full characterization of the maximum achievable rate for convolutional codes with any given column distance, column span and decoding delay. Numerical results demonstrate that the optimal streaming codes outperform existing streaming codes of comparable complexity over some instances of the Gilbert-Elliott channel and the Fritchman channel.

cs.IT↗

arXiv2017

Improved Utility-based Congestion Control for Delay-Constrained Communication

Due to the presence of buffers in the inner network nodes, each congestion event leads to buffer queueing and thus to an increasing end-to-end delay. In the case of delay sensitive applications, a large delay might not be acceptable and a solution to properly manage congestion events while maintaining a low end-to-end delay is required. Delay-based congestion algorithms are a viable solution as they target to limit the experienced end-to-end delay. Unfortunately, they do not perform well when sharing the bandwidth with congestion control algorithms not regulated by delay constraints (e.g., loss-based algorithms). Our target is to fill this gap, proposing a novel congestion control algorithm for delay-constrained communication over best effort packet switched networks. The proposed algorithm is able to maintain a bounded queueing delay when competing with other delay-based flows, and avoid starvation when competing with loss-based flows. We adopt the well-known price-based distributed mechanism as congestion control, but: 1) we introduce a novel non-linear mapping between the experienced delay and the price function and 2) we combine both delay and loss information into a single price term based on packet interarrival measurements. We then provide a stability analysis for our novel algorithm and we show its performance in the simulation results carried out in the NS3 framework. Simulation results demonstrate that the proposed algorithm is able to: achieve good intra-protocol fairness properties, control efficiently the end-to-end delay, and finally, protect the flow from starvation when other flows cause the queuing delay to grow excessively.

cs.NI↗