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UplinkNet: Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction

UplinkNet: Practical Commercial 5G Standalone (SA) Uplink Th...

引用

IEEE visual communications and image processing (VCIP)

作者： Kasidis Arunruangsirilert Jiro Katto Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

While 5G New Radio (NR) networks offer significant uplink throughput improvements, these gains are primarily realized when User Equipment (UE) connects to high-frequency millimeter wave (mmWave) bands. The growing demand for uplink-intensive applications, such as real-time UHD 4K/8K video streaming and Virtual Reality (VR)/Augmented Reality (AR) content, highlights the need for accurate uplink throughput prediction to optimize user Quality of Experience (QoE). In this paper, we introduce UplinkNet, a compact neural network designed to predict future uplink throughput using past throughput and RF parameters available through the Android API. With a model size limited to approximately 4,000 parameters, UplinkNet is suitable for IoT and low-power devices. The network was trained on real-world drive test data from commercial 5G Standalone (SA) networks in Tokyo, Japan, and Bangkok, Thailand, across various mobility conditions. To ensure practical implementation, the model uses only Android API data and was evaluated on unseen data against other models. Results show that UplinkNet achieves an average prediction accuracy of 98.9% and an RMSE of 5.22 Mbps, outperforming all other models while maintaining a compact size and low computational cost.

关键词： Solid modeling Accuracy visual communication Computational modeling Virtual reality Throughput Data models Quality of experience Uplink Millimeter wave communication

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Histogram Guided image Binning Based Plug-In Module for Low-Light HDR Reconstruction

Histogram Guided Image Binning Based Plug-In Module for Low-...

引用

International conference on Imaging, Signal processing and Communication (ICISPC)

作者： Zheyi Li Fengshan Zhao Haorong Jiang Qin Liu Takeshi Ikenaga Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Software Institute Nanjing University Nanjing China

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

Single image high dynamic range image reconstruction has been receiving much attention for recovering image details and showing the possibility of simulating brightness distribution in the real world. While most current works focus on recovering overexposed areas, this work is more focused on underexposed regions and the brightness adjustment of the whole image. This paper proposes an additional plug-in module with histogram guided image binning method for low-light image high dynamic range restoration. This plug-in module is mainly designed with histogram feature extraction and image binning based brightness restoration, enhancing the recovery for the darker regions. Extensive experimentation demonstrates the effectiveness of the approach in enhancing the visual quality of low-light images and preserving details in underexposed areas. At an extremely low-light condition, networks using this plug-in module achieve up to a 0.8227 PSNR improvement and a 0.8278 PU21-PSNR improvement.

关键词： Histograms visualization Brightness Imaging Signal processing Feature extraction Stability analysis

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PFT-ILF: In-loop Filter with Partition Feature Transform for Versatile Video Coding

PFT-ILF: In-loop Filter with Partition Feature Transform for...

引用

IEEE visual communications and image processing (VCIP)

作者： Xin-Yi Cui Zhikai Liu Zhidao Zhou Li Chen Fan Liang School of Electronics and Information Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The new generation of video coding standards, Versatile Video Coding (VVC), integrates a range of loop filter mechanisms, notably the De-Blocking Filter (DBF), Sample Adaptive Offset (SAO), and Adaptive Loop Filter (ALF). However, these traditional tools are handcrafted empirically and have limitations. Thus many CNN-based loop filters have been proposed to achieve better image quality. In this paper, we propose a novel network based on Coding Unit (CU) partition feature transformation, called PFT-ILF. Considering that the CU partition map contains image distortion information, our approach innovatively utilizes the CU partition map as prior information to guide filtering. And we design a Partition Feature Transform (PFT) layer, which uses partition features to generate modulation parameters pair for adjusting the features of several intermediate layers in the network. We integrate our proposed filter into the NNVC standard software VTM11.0_NNVC-4.0 and conduct ablation experiments. Under the all intra configuration, our method achieves Bjøntegaard-Delta Bit-Rate (BD-BR) reductions of 7.52%, 17.36%, and 18.65% for Y, U, and V components, respectively.

关键词： Video coding image coding Adaptive filters Transforms Artificial neural networks Information filters Distortion Software image restoration Standards

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Learning Systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

引用

1st International conference on Intelligent Systems in Computing and communications, ISCComm 2023

作者： Shetty, Sudheer Ganesh, Devadiga Likhit kumar Poojary, Rahul R. Raviraj Tejas, R. Hegde, Nagaratna P. Vikkurty, Sireesha Department of ISE Sahyadri College of Engineering & Management Mangaluru Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of CSE Vasavi College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031756078

In an era dominated by the ubiquitous sharing of visual content on social media, the proliferation of fake photographs and digital fakes has emerged as a critical issue. This paper explores how fake images are detected with fusion of by various techniques of image processing, CNN, and machine learning, presenting an innovative approach to address the intricate challenge of identifying manipulated images. This paper gives insight on various approaches of image processing techniques and state-of-the-art machine learning algorithms used in a robust forgery image detection system. A comprehensive framework is proposed on various image forensic methods, ensuring the precise detection of fake images across diverse contexts. The paper also emphasizes the impact of countering the dissemination of fake photographs to preserve the credibility of digital media. This combination of image processing, CNN and machine learning propels the ongoing quest to uncover the truth behind the surge of digital fakes in today's digital landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration

Benchmarking Conventional and Learned Video Codecs with a Lo...

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IEEE visual communications and image processing (VCIP)

作者： Siyue Teng Yuxuan Jiang Ge Gao Fan Zhang Thomas Davis Zoe Liu David Bull Visual Information Laboratory University of Bristol Bristol United Kingdom Visionular Inc. Los Altos CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount of system delay (e.g., Random Access mode in MPEG codecs), which is not always acceptable for live delivery. This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration. Specifically, this study includes two MPEG standard codecs (H.266/VVC VTM and JVET ECM), two AOM codecs (AV1 libaom and AVM), and two recent neural video coding models (DCVC-DC and DCVC-FM). To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space. The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested, with a 16.1% (based on PSNR) average BD-rate saving over AOM AVM, and 11.0% over DCVC-FM. We also observed inconsistent performance with the learned video codecs, DCVC-DC and DCVC-FM, for test content with large background motions.

关键词： Video coding MPEG standards image coding visual communication Transform coding Video compression Delays Complexity theory Video codecs Standards

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Generalized Zero-shot Learning Based on Dual Latent Space Reconstruction

Generalized Zero-shot Learning Based on Dual Latent Space Re...

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2022 International conference on Intelligent Systems, communications, and Computer Networks, ISCCN 2022

作者： Xu, Yangdongfang Yang, Guan Liu, Xiaoming Liu, Yang School of Computer Science Zhongyuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Zhengzhou Key Laboratory of Text Processing and Image Understanding Zhengzhou China School of Telecommunications Engineering Xidian University Xi’an China

ISBN: (纸本)9781510657342

Generalized Zero-Shot Learning (GZSL) is characterized as a training process that comprises visual samples from seen classes and semantic samples from seen and unseen classes, followed by a testing process that classifies visual samples from seen and unseen classes. Existing zero-shot learning (ZSL) approaches suffer from domain shift and information loss issues as a result of class differences between visible and unseen classes, as well as uneven image distribution. In this study, a generalized zero-shot learning strategy based on dual latent space reconstruction (DLR-GZSL) is proposed. The method aims to establish a latent space of shared semantic and visual information, uses dual learning to align different modal representations to alleviate the domain shift problem, uses triplet loss to improve intra-class diversity and inter-class separability of the generated samples, and uses information bottleneck to retain as much valid generated feature information as possible to reduce information loss. Experiments on the CUB, SUN, AWA1, and AWA2 datasets reveal that the suggested method has more accurate than previous methods, demonstrating its effectiveness. © 2022 SPIE.

关键词： Zero-shot learning

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Advanced Learning-Based Inter Prediction for Future Video Coding

Advanced Learning-Based Inter Prediction for Future Video Co...

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IEEE visual communications and image processing (VCIP)

作者： Yanchen Zhao Wenhong Duan Chuanmin Jia Shanshe Wang Siwei Ma School of Computer Science Peking University China Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In the fourth generation Audio Video coding Standard (AVS4), the Inter Prediction Filter (INTERPF) reduces discontinuities between prediction and adjacent reconstructed pixels in inter prediction. The paper proposes a low complexity learning-based inter prediction (LLIP) method to replace the traditional INTERPF. LLIP enhances the filtering process by leveraging a lightweight neural network model, where parameters can be exported for efficient inference. Specifically, we extract pixels and coordinates utilized by the traditional INTERPF to form the training dataset. Subsequently, we export the weights and biases of the trained neural network model and implement the inference process without any third-party dependency, enabling seamless integration into video codec without relying on Libtorch, thus achieving faster inference speed. Ultimately, we replace the traditional handcraft filtering parameters in INTERPF with the learned optimal filtering parameters. This practical solution makes the combination of deep learning encoding tools with traditional video encoding schemes more efficient. Experimental results show that our approach achieves 0.01%, 0.31%, and 0.25% coding gain for the Y, U, and V components under the random access (RA) configuration on average.

关键词： Video coding Training image coding Filtering visual communication Computational modeling Neural networks Complexity theory Video codecs Standards

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

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

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IEEE visual communications and image processing (VCIP)

作者： Jakub Nawała Yuxuan Jiang Fan Zhang Xiaoqing Zhu Joel Sole David Bull Visual Information Laboratory University of Bristol Bristol United Kingdom Netflix Inc. Los Gatos CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

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://***/fan-aaron-zhang/bvi-aom.

关键词： Training Video coding image coding visual communication Video compression Performance gain Video codecs Spatial resolution Optimization Standards

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Perceptual Quality Assessment for Video Frame Interpolation

Perceptual Quality Assessment for Video Frame Interpolation

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IEEE visual communications and image processing (VCIP)

作者： Jinliang Han Xiongkuo Min Yixuan Gao Jun Jia Lei Sun Zuowei Cao Yonglin Luo Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Tencent China

The quality of frames is significant for both research and application of video frame interpolation (VFI). In recent VFI studies, the methods of full-reference image quality assessment have generally been used to evaluate the quality of VFI frames. However, high frame rate reference videos, necessities for the full-reference methods, are difficult to obtain in most applications of VFI. To evaluate the quality of VFI frames without reference videos, a no-reference perceptual quality assessment method is proposed in this paper. This method is more compatible with VFI application and the evaluation scores from it are consistent with human subjective opinions. A new quality assessment dataset for VFI was constructed through subjective experiments firstly, to assess the opinion scores of interpolated frames. The dataset was created from triplets of frames extracted from high-quality videos using 9 state-of-the-art VFI algorithms. The proposed method evaluates the perceptual coherence of frames incorporating the original pair of VFI inputs. Specifically, the method applies a triplet network architecture, including three parallel feature pipelines, to extract the deep perceptual features of the interpolated frame as well as the original pair of frames. Coherence similarities of the two-way parallel features are jointly calculated and optimized as a perceptual metric. In the experiments, both full-reference and no-reference quality assessment methods were tested on the new quality dataset. The results show that the proposed method achieves the best performance among all compared quality assessment methods on the dataset.

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IEEE visual communications and image processing (VCIP)

作者： Ziyin Huang Yui-Lam Chan Ngai-Wing Kwong Sik-Ho Tsang Kin-Man Lam Wing-Kuen Ling Department of Electronic and Information Engineering The Hong Kong Polytechnic University School of Information Engineering Guangdong University of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Compressed screen content videos often exhibit artifacts in edge areas and suffer from distortions during scene switches, where content abruptly changes between frames. Existing multi-frame models, which use a fixed range of neighbor frames, struggle with these switches. To address this, we propose a novel method that effectively handles scene switches. Our approach utilizes Long-term Feature Extraction (LFE) to capture contextual information, while the Frame Similarity-based Short-term Feature Extraction (FSFE) focuses on texture information to manage fast motion and scene switches. In FSFE, a Similarity-based Neighbor Frame Selector (SNFS) is designed to choose relevant neighbor frames for the short-term stream, enhancing the quality of scene switch frames. To fuse short-term and long-term features adaptively, we introduce a local-spatial and global-channel attention module, which recalibrates spatial and channel-wise feature responses. Experimental results show that our Frame Similarity-Based via Adaptive Long Short-Term Fusion (FSLST) method significantly improves the quality of compressed videos, outperforming current state-of-the-art methods.

关键词： image coding Fuses visual communication Streaming media Feature extraction Distortion Quality assessment Videos

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