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Inter-Frame Coding for Dynamic Meshes via Coarse-to-Fine Anchor Mesh Generation

Inter-Frame Coding for Dynamic Meshes via Coarse-to-Fine Anc...

引用

IEEE visual communications and image processing (VCIP)

作者： He Huang Lizhi Hou Qi Yang Yiling Xu Shanghai Jiao Tong University University of Missouri-Kansas City

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In the current Video-based Dynamic Mesh Coding (V-DMC) standard, inter-frame coding is restricted to mesh frames with constant topology. Consequently, temporal redundancy is not fully leveraged, resulting in suboptimal compression efficacy. To address this limitation, this paper introduces a novel coarse-to-fine scheme to generate anchor meshes for frames with time-varying topology. Initially, we generate a coarse anchor mesh using an octree-based nearest neighbor search. Motion estimation compensates for regions with significant motion changes during this process. However, the quality of the coarse mesh is low due to its suboptimal vertices. To enhance details, the fine anchor mesh is further optimized using the Quadric Error Metrics (QEM) algorithm to calculate more precise anchor points. The inter-frame anchor mesh generated herein retains the connectivity of the reference base mesh, while concurrently preserving superior quality. Experimental results show that our method achieves 7.2% ∼ 10.3% BD-rate gain compared to the existing V-DMC test model version 7.

关键词： image coding Quantization (signal) visual communication Motion estimation Redundancy Nearest neighbor methods Encoding Topology Resource management Standards

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Composable image Coding for Machine via Task-oriented Internal Adaptor and External Prior

Composable Image Coding for Machine via Task-oriented Intern...

引用

IEEE visual communications and image processing (VCIP)

作者： Jinming Liu Xin Jin Ruoyu Feng Zhibo Chen Wenjun Zeng Shanghai Jiao Tong University Eastern Institute of Technology Ningbo Ningbo Institute of Digital Twin University of Science and Technology of China

Traditional image coding standards are typically optimized with a focus on human perception, which conflicts with the fact that most of the images are now analyzed by machines. To enable a variety of downstream intelligent tasks, contemporary approaches either utilize traditional codecs for image compression which are then used for task analysis, or develop a unified feature compression paradigm with deep learning techniques. However, they might suffer from accumulative errors and poor compatibility/generalization due to the conflict between standardized codecs and diverse machine tasks. We argue that a favorable image coding for machine (ICM) framework should have highly efficient adaptation capability, and take the ultimate task goals into account. Oriented at this, we propose a composable ICM solution dubbed Com-ICM, which develops plug-and-play lightweight internal adaptors injected into the codec architecture for efficient task transfer, and leverages off-the-shelf (large) models to provide external prior information for further task-oriented semantics learning. The internal adaptors (from the architectural aspect) and external priors (from the precondition aspect) complement each other, resulting in a mutually beneficial effect. We evaluate Com-ICM on diverse vision benchmarks, including image classification, object detection, and semantic segmentation, demonstrating its effectiveness and superiority. We are also actively submitting Com-ICM as a technical proposal to the international organization for standardization.

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Perceptual Quality Assessment for Digital Human Heads 48

Perceptual Quality Assessment for Digital Human Heads

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Zicheng Zhou, Yingjie Sun, Wei Min, Xiongkuo Wu, Yuzhe Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China Peng Cheng Laboratory China Donghua University College of Information Science & Technology China

ISBN: (纸本)9781728163277

Digital humans are attracting more and more research interest during the last decade, the generation, representation, rendering, and animation of which have been put into large amounts of effort. However, the quality assessment of digital humans has fallen behind. Therefore, to tackle the challenge of digital human quality assessment issues, we propose the first large-scale quality assessment database for three-dimensional (3D) scanned digital human heads (DHHs). The constructed database consists of 55 reference DHHs and 1,540 distorted DHHs along with the subjective perceptual ratings. Then, a simple yet effective full-reference (FR) projection-based method is proposed to evaluate the visual quality of DHHs. The pretrained Swin Transformer tiny is employed for hierarchical feature extraction and the multi-head attention module is utilized for feature fusion. The experimental results reveal that the proposed method exhibits state-of-the-art performance among the mainstream FR metrics. The database is released at https://***/zzc-1998/DHHQA. © 2023 IEEE.

关键词： Digital human head full-reference projection-based quality assessment database

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READFake: Reflection and Environment-Aware DeepFake Detection

READFake: Reflection and Environment-Aware DeepFake Detectio...

引用

IEEE visual communications and image processing (VCIP)

作者： Muhammad Mohzary Elham Basunduwah Sejun Song Baek-Young Choi Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper presents a novel Reflection and Environment-Aware DeepFake (READFake) detection technique. Using reflections on various body parts (e.g., eyes, nose, cheeks, etc.) and environmental factors, we validate the hypothesis that the existing DeepFake creation methods, including reenactment, replacement, and synthesis, fail to coordinate their counterfeits with the reflective components along with the given environmental mapping. We detect various features from the specular highlight images, including color components, shapes, and textures, to check the coordination with the surrounding environmental factors, such as indoor/outdoor, bright/dark backgrounds, and light strength. We have conducted extensive experiments to evaluate the performance of READFake using various input parameters and advanced Deep Neural Network (DNN) architectures on multiple public DeepFake datasets. The empirical results show that READFake achieves high accuracy (99.00%) in detecting sophisticated DeepFake images.

关键词： Deepfakes Accuracy Shape image color analysis visual communication Nose Artificial neural networks Feature extraction Reflection Environmental factors

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Tympanic Membrane Generation with Generative Adversarial Networks 29

Tympanic Membrane Generation with Generative Adversarial Net...

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29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Eseoglu, Mustafa Furkan Karsligil, M. Elif Kocak, Ismail Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu Akilli Sistemler Lab Istanbul Turkey DrVoice Clin Istanbul Turkey

ISBN: (纸本)9781665436496

Obtaining sufficient original data in most studies in the field of medical pattern recognition is a difficult and time consuming process. Different data augmentation methods are used to increase the amount of data to be used to train these systems. In this study, a generative adversarial networks based system that produces artificial images by using the tympanic membrane images taken from otoscope devices for data augmentation has been designed and trained. Artificial images that has been generated by different generative adversarial networks have been evaluated by specialist physicians with a visual Turing test. Preliminary results show that artificial medical images can be perceived as real with high confidence scores.

关键词： generative adversarial networks medical image processing data augmentation visual Turing test

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引用

IEEE visual communications and image processing (VCIP)

作者： Haihang Wang Feng Yuan Liqian Zhang Jixing Chen Fu Lee Wang Zhaoqing Pan School of Electrical and Information Engineering Tianjin University Tianjin China School of Science and Technology Hong Kong Metropolitan University Hong Kong China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The Versatile Video Coding (VVC) is the latest video coding standard that includes advanced intra coding techniques such as the chroma separate tree and cross-component linear model to improve the prediction accuracy of chroma components. However, these sophisticated techniques inevitably increase the computational complexity of chroma intra coding. In this paper, we propose a fast chroma intra-prediction mode decision algorithm to reduce the computational complexity of the VVC encoder. Firstly, the mode selection correlation between the current chroma Coding Unit (CU) and its nearest-encoded chroma CU is analyzed. Then, based on the variation of the sum of modulus differences, an early mode decision is proposed for the current chroma CU to determine whether the best coding mode should be traversed or directly inherited from its nearest encoded chroma CU. The experimental results show that the proposed algorithm significantly reduces the computational complexity of chroma intra-prediction and achieves an average of 28% chroma intra encoding time saving with only 0.08-1.43% performance degradation in terms of BD-rate.

关键词： Video coding Degradation image coding Correlation visual communication Predictive models Prediction algorithms Encoding Computational complexity Standards

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Swin Transformer based Siamese Network for Thermal and Optical image Registration

Swin Transformer based Siamese Network for Thermal and Optic...

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IEEE Signal processing and communications Applications (SIU)

作者： Mohamed Elsaeidy İsmail Can Yağmur Hasan Fehmi Ateş Bahadir Kürşat Güntürk Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Computer Science Ozyegin University Istanbul Turkey

The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.

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Alignment of image-Text and Video-Text Datasets

Alignment of Image-Text and Video-Text Datasets

引用

IEEE Signal processing and communications Applications (SIU)

作者： Yunus Emre Özköse Zeynep Gökçe Pınar Duygulu Bilgisayar Mühendisliği Hacettepe Üniversitesi Ankara Türkiye

In this study, the alignment of video-text and imagetext datasets is studied. Firstly, similarities are calculated over the texts in the two data sets. A retrieval setup with visual similarities is then applied to the subset which is created via calculated text similarities. A BERT-based embedding vector method is applied to the raw and pure texts. As a visual feature, object-based and CLIP-based methods are used to define video frames. According to the results, alignment with CLIP features achieves the best results in the subset created by filtering using raw text.

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Lightweight Arbitrary-Scale Super-Resolution of Remote Sensing images via Super-Scale Feature

Lightweight Arbitrary-Scale Super-Resolution of Remote Sensi...

引用

IEEE visual communications and image processing (VCIP)

作者： Yifei Long Yuantong Zhang Daiqin Yang Zhenzhong Chen Huairui Wang Shan Liu School of Remote Sensing and Information Engineering Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Remote sensing image (RSI) super-resolution (SR) demands lightweight and efficient methods due to required rapid response in practical applications. Integrating RSIs with different resolutions for diverse applications also requires arbitrary-scale SR, making fix-scaled SR scale inflexible. Therefore, a lightweight SR algorithm capable of arbitrary-scale is necessary for RSIs. To address the above issue, a super-scale feature-based lightweight arbitrary-scale (SFLA) SR network is proposed in this paper. The network consists of two modules: 1) A super-scale feature extraction (SSFE) module that extracts features at both the initial low-resolution (LR) and an integer super-scale resolution, 2) A self-attention implicit function reconstruction (SIFR) module that utilizes multi-layer perceptron (MLP) network and self-attention mechanism for pixel-wise feature mapping to achieve superior SR results. Comparative experiments and ablation results demonstrate that the proposed SFLA algorithm effectively strikes a good balance between performance and complexity.

关键词： visual communication Superresolution Feature extraction Complexity theory Remote sensing image reconstruction

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Sample Adaptive Offset for Geometry-based Point Cloud Attribute Compression

Sample Adaptive Offset for Geometry-based Point Cloud Attrib...

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IEEE visual communications and image processing (VCIP)

作者： Lizhi Hou Qian Zhang Yiling Xu Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Geometry-based Point Cloud Compression (G-PCC) has demonstrated remarkable coding efficiency across a wide range of datasets. However, due to the irreversible quantization loss, the G-PCC reconstructed attribute is inevitably distorted. In this paper, we propose a Sample Adaptive Offset (SAO) method for G-PCC to reduce attribute distortion. Specifically, after the regular attribute coding of G-PCC, the whole point cloud is first classified according to their distorted attribute values to maintain a low-complexity design. The cross-channel correlation is also utilized to enhance the classification process. Then, one optimal offset is derived for each class by a Rate Distortion Optimization (RDO). A second stage RDO is performed to ensure the maximum positive gain. Finally, the selected classes and their corresponding offsets are applied to the reconstructed attribute to reduce the distortion. We integrate the proposed method on top of the G-PCC solid point cloud test model GeSTM_v6.0. The simulation results show that the proposed method can achieve up to {0.4%, 6.3%, 6.1%} attribute Bjøntegaard-Delta (BD)-rate gain on different channels with negligible additional complexity on the encoding and decoding side.

关键词： Point cloud compression Solid modeling image coding visual communication Simulation Rate-distortion Distortion Solids Encoding image reconstruction

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