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Complex-valued SAR image Compression: A Novel Approach for Amplitude and Phase Recovery

Complex-valued SAR Image Compression: A Novel Approach for A...

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IEEE visual communications and image processing (VCIP)

作者： Paras Maharjan Zhu Li University of Missouri Kansas City

Synthetic Aperture Radar (SAR) is an active and coherent imaging system that utilizes radio waves to illuminate the Earth’s surface, generating complex-valued images of the ground. It is widely employed in environmental monitoring and surveillance due to its ability to function effectively in diverse weather and lighting conditions, including day and night. SAR achieves this through the emission of coherent radio frequency signals and the capture of slant range and azimuth information. These details are stored as a complex signal in the form of Inphase (I) and Quadrature (Q) components, known as complex-valued SAR. The complex values (I/Q) can be transformed into amplitude and phase information. SAR images have a substantial size and a wide dynamic range, which pose challenges for storage and transmission. Thus, the adoption of compression techniques becomes essential to manage the large size of SAR images. In contrast to existing methods that primarily compress amplitude information, our study presents a novel pipeline to compress complex-valued SAR images using a prediction-recovery framework that utilizes a conventional image compression algorithm, High-Efficiency Video Codec (HEVC), to encode the original image to a bitstream. Subsequently, the framework decodes a prediction of the I/Q channels and then recovers the phase and amplitude via SARRecoveryNet, a deep-learning-based network, to effectively remove compression artifacts and recover information that suits different applications. By directly compressing the I/Q values as a predictor, and recovery via a deep learning module, our method preserves both the amplitude and phase information of the SAR image as part of the loss function in the training. Experimental results demonstrate that our proposed approach significantly enhances complex-valued SAR image compression in terms of rate-distortion (RD) performance and visual perception. The code will be made available on GitHub after the paper is accepted.

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Diagnosis-oriented Medical image Compression with Efficient Transfer Learning

Diagnosis-oriented Medical Image Compression with Efficient ...

引用

IEEE visual communications and image processing (VCIP)

作者： Guangqi Xie Xin Li Xiaohan Pan Zhibo Chen Unviersity of Science and Technology of China Hefei China

Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices. In this process, a large amount of redundant content irrelevant to the diagnosis is subjected to high-fidelity coding, leading to unnecessary transmission costs. To mitigate this, we propose diagnosis-oriented medical image compression, a special semantic compression task designed for medical scenarios, targeting to reduce the compression cost without compromising the diagnosis accuracy. However, collecting sufficient medical data to optimize such a compression system is significantly expensive and challenging due to privacy issues and the lack of professional annotation. In this study, we propose DMIC, the first efficient transfer learning-based codec, for diagnosis-oriented medical image compression, which can be effectively optimized with only few-shot annotated medical examples, by reusing the knowledge in the existing reinforcement learning-based task-driven semantic coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the partial parameters of the policy network for bit allocation within HRLVSC, which enables it to adapt to the medical images. In this work, we validate our DMIC with the typical medical task, Coronary Artery Segmentation. Extensive experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of the policy network with only 1 medical sample.

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Effectiveness Evaluation of Basic Bitstream Features in Surveillance Video Recompression Forensics Using VVC

Effectiveness Evaluation of Basic Bitstream Features in Surv...

引用

IEEE visual communications and image processing (VCIP)

作者： Yanchao Gong Kunlei Li Xiaokai Zhou Jiaxi Jing Kaifang Yang Ying Liu School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

Surveillance videos have been widely used in criminal investigation, judicial evidence collection, and other fields. The authenticity of surveillance video is a key prerequisite for whether the surveillance video is used legally. Video recompression forensics (VRF) is a widely recognized technique for video authenticity authentication. And basic bitstream feature (BBF) extraction is the primary step in VRF, which significantly affects the accuracy of VRF. The current VRF methods have not systematically focused on the different impact of different types of BBF on VRF. Most VRF methods directly extract the BBF from video bitstream to construct the advanced bitstream features, which implies that all BBF have an equal impact on the accuracy of VRF. This operation is obviously unreasonable. This paper focuses on VVC encoded surveillance videos, and an experimental scheme is first designed to evaluate the effectiveness of BBF. Based on the experimental data, the impact of different types of BBF on the accuracy of VRF is further analyzed and evaluated, and some important conclusions are drawn. These conclusions have important guiding significance for the subsequent development of efficient VRF methods suitable for surveillance videos using VVC.

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Split-Conv: A Resource-efficient Compression Method for image Quality Assessment Models

Split-Conv: A Resource-efficient Compression Method for Imag...

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IEEE visual communications and image processing (VCIP)

作者： Yiming Zhang Xiongkuo Min Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

Blind image Quality Assessment (BIQA) models based on deep neural networks (DNNs) have achieved state-of-the-art performance recently. However, the heavyweight architecture makes them hard to deploy on resource-constrained devices. Filter pruning is one of the most effective ways to compress the DNN model. However, most pruning methods need complete retraining after pruning which is too resource-consuming for IQA models with complex training processes. In this paper, we propose a resource-efficient structural pruning method for IQA models called Split-Conv, where the model only needs to be retrained on small IQA databases. Specifically, we split convolutional layers into sub-convolutional kernels and decorators, which can measure the importance of convolutional channels more precisely, thus reducing the requirement for retraining conditions. The experiments on several IQA models demonstrate the effectiveness of our IQA model pruning approach. For instance, DBCNN, StairIQA, and HyperIQA’s performances can be kept at the baseline level when 50% parameters are pruned with the model being retrained on the authentically distort IQA database containing only 586 images, and the performances of StairIQA and HyperIQA on KonIQ-10k database are still acceptable after 80% of the parameters have been pruned. Besides, due to Split-Conv’s capability to identify the important, useless, and harmful filters, the performances of some BIQA models can even be boosted after model compression with a proper pruning ratio.

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

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IEEE visual communications and image processing (VCIP)

作者： Da Ai Siyu Qin Zihe Nie Hui Yuan Ying Liu Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA- Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： Point cloud compression Three-dimensional displays visual communication Convolution Semantic segmentation Semantics Transformers Feature extraction Data mining Testing

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Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting

Localization-Aware Multi-Scale Representation Learning for R...

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IEEE visual communications and image processing (VCIP)

作者： Sujia Wang Xiangwei Shen Yansong Tang Xin Dong Wenjia Geng Lei Chen Shenzhen International Graduate School Tsinghua University Department of Automation Tsinghua University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Repetitive action counting (RAC) aims to estimate the number of class-agnostic action occurrences in a video without exemplars. Most current RAC methods rely on a raw frame-to-frame similarity representation for period prediction. However, this approach can be significantly disrupted by common noise such as action interruptions and inconsistencies, leading to sub-optimal counting performance in realistic scenarios. In this paper, we introduce a foreground localization optimization objective into similarity representation learning to obtain more robust and efficient video features. We propose a Localization-Aware Multi-Scale Representation Learning (LMRL) framework. Specifically, we apply a Multi-Scale Period-Aware Representation (MPR) with a scale-specific design to accommodate various action frequencies and learn more flexible temporal correlations. Furthermore, we introduce the Repetition Foreground Localization (RFL) method, which enhances the representation by coarsely identifying periodic actions and incorporating global semantic information. These two modules can be jointly optimized, resulting in a more discerning periodic action representation. Our approach significantly reduces the impact of noise, thereby improving counting accuracy. Additionally, the framework is designed to be scalable and adaptable to different types of video content. Experimental results on the RepCountA and UCFRep datasets demonstrate that our proposed method effectively handles repetitive action counting.

关键词： Representation learning Location awareness Adaptation models Correlation visual communication image processing Noise Semantics Optimization Monitoring

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Handling Class Imbalance in Black-Box Unsupervised Domain Adaptation with Synthetic Minority Over-Sampling

Handling Class Imbalance in Black-Box Unsupervised Domain Ad...

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IEEE visual communications and image processing (VCIP)

作者： Yawen Zou Chunzhi Gu Zi Wang Guang Li Jun Yu Chao Zhang Graduate School of Science & Engineering University of Toyama Toyama Japan Department of Computer Science & Engineering Toyohashi University of Technology Toyohashi Japan Institute of Science & Technology Niigata University Niigata Japan MDS Center Hokkaido University Hokkaido Japan Faculty of Engineering University of Toyama Toyama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Black-box unsupervised domain adaptation (BBUDA) is a challenging task that transfers knowledge from the source domain to the target domain without access to the source data and source model, thus alleviating public concerns about data security. However, BBUDA requires the source model to function as a black-box predictor for the target data, and the pseudo-labels often exhibit class imbalance, which degrades the performance. To tackle this problem, we propose employing the synthetic minority oversampling technique (SMOTE) and adaptive sampling to rebalance data. Given that predictions often contain errors, we first select reliable high-confidence data before using SMOTE to generate synthetic samples for the minority class. Second, we incrementally select high-confidence data from the remaining low-confidence data with an adaptive sampling rate for each class, in which the minority class (with the fewest samples) is assigned a higher sampling rate and the majority class (with the most samples) is assigned a lower sampling rate. The experimental results demonstrate that our method can mitigate the class imbalance and further improve the performance of the target model.

关键词： Adaptation models Accuracy visual communication image processing Data security Closed box Predictive models Data models Reliability

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Content-Adaptive Rate-Quality Curve Prediction Model in Media processing System

Content-Adaptive Rate-Quality Curve Prediction Model in Medi...

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IEEE visual communications and image processing (VCIP)

作者： Shibo Yin Zhiyu Zhang Peirong Ning Qiubo Chen Jing Chen Quan Zhou Guo Lu Li Song Xiaohongshu Inc China Shanghai Jiao Tong University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In streaming media services, video transcoding is a common practice to alleviate bandwidth demands. Unfortunately, traditional methods employing a uniform rate factor (RF) across all videos often result in significant inefficiencies. Content-adaptive encoding (CAE) techniques address this by dynamically adjusting encoding parameters based on video content characteristics. However, existing CAE methods are often tightly coupled with specific encoding strategies, leading to inflexibility. In this paper, we propose a model that predicts both RF-quality and RF-bitrate curves, which can be utilized to derive a comprehensive bitrate-quality curve. This approach facilitates flexible adjustments to the encoding strategy without necessitating model retraining. The model leverages codec features, content features, and anchor features to predict the bitrate-quality curve accurately. Additionally, we introduce an anchor suspension method to enhance prediction accuracy. Experiments confirm that the actual quality metric (VMAF) of the compressed video stays within ±1 of the target, achieving an accuracy of 99.14%. By incorporating our quality improvement strategy with the rate-quality curve prediction model, we conducted online A/B tests, obtaining both +0.107% improvements in video views and video completions and +0.064% app duration time. Our model has been deployed on the Xiaohongshu App.

关键词： Radio frequency image coding Accuracy Codecs visual communication Transcoding Streaming media Predictive models Feature extraction Testing

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Design and Implementation of Railway Track Foreign Object image processing Platform Based on GUI

Design and Implementation of Railway Track Foreign Object Im...

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International conference on Computers, communications, and Systems (ICCCS)

作者： Xiqian LEI Tiantian XU Liqun BAO Ying LI Electronic Information Engineering College LanZhou Institute of Technology Lanzhou China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Railway plays a leading role in the field of transportation in China and shoulders the important mission of driving the development of national economy. In view of the changeable environment of railway track, the processing algorithm with good real-time performance, strong robustness and high accuracy in the process of foreign body detection is the key to achieve rapid detection. In order to facilitate researchers to compare and analyze the effects of relevant algorithms intuitively and quickly, the GUI visual interactive interface was used to design the simulation platform for track foreign object image processing, conceive the design process, build the overall design of the platform, and divide the static foreign object detection and moving target detection modules according to the realization functions. In the static foreign body detection module, the image of track foreign body is input to determine whether there is track foreign body and give early warning. Meanwhile, the processing effect of different algorithms can be visually compared through the operation results. In the moving target detection module, the moving target in the input video is marked and tracked. The test results of the simulation platform show that the processing platform is simple and easy to operate, and can effectively assist researchers to deepen the understanding and application of orbital foreign object image processing.

关键词： visualization Target tracking image processing Transportation Object detection Rail transportation Robustness

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Joint Watermarking-Encryption Scheme Utilizing Transform Domain and Advanced Encryption Standard

Joint Watermarking-Encryption Scheme Utilizing Transform Dom...

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2022 IEEE International conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Nouioua, Nadhir Seddiki, Ali Ghaz, Abdelkrim Djillali Liabes University Telecommunications and Digital Signal Processing Laboratory Sidi Bel-Abbes Algeria Djillali Liabes University Communications Networks Architecture and Multimedia Laboratory Sidi Bel-Abbes Algeria

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this research, a digital image joint watermarking-encryption method based on Lifting Wavelet Transform (LWT) for cover image decomposition and the Advanced Encryption Standard (AES) for embedded text protection ensuring in the Hue, Saturation, Intensity (HSI) color space is suggested. The method is about embedding a cipher-text into the intensity component of the cover image as it is in contrast with the hue and saturation, where this last was decomposed utilizing the LWT, and the Low-Low sub-band was exploited for text embedding, the intensity is the less perceptible component for the human visual system. The incorporated text was encrypted using the AES, which provides high robustness against tampering and spying using a key distinguished from the Singular Value Decomposition (SVD) decomposition. The experimental results extrapolate that the proposed method can provide a high imperceptibility. © 2022 IEEE.

关键词： Singular value decomposition

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