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检索条件"任意字段=Conference on Visual Communications and Image Processing 2002"

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Security and Forensics Exploration of Learning-based image Coding

Security and Forensics Exploration of Learning-based Image C...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Bhowmik, Deepayan Elawady, Mohamed Nogueira, Keiller Univ Stirling Div Comp Sci & Math Stirling FK9 4LA Scotland

ISBN: (纸本)9781728185514

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.

关键词： Media forensics security learning based image coding JPEG AI DNN watermarking source identification

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No-reference Stereoscopic image Quality Assessment Based on Parallel Multi-scale Perception

No-reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Ziyi Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781665475921

With the rapid development of 3D technologies, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods are in great demand. In this paper, we propose a parallel multi-scale feature extraction convolution neural network (CNN) model combined with novel binocular feature interaction consistent with human visual system (HVS). In order to simulate the characteristics of HVS sensing multi-scale information at the same time, parallel multi-scale feature extraction module (PMSFM) followed by compensation information is proposed. And modified convolutional block attention module (MCBAM) with less computational complexity is designed to generate visual attention maps for the multi-scale features extracted by the PMSFM. In addition, we employ cross-stacked strategy for multi-level binocular fusion maps and binocular disparity maps to simulate the hierarchical perception characteristics of HVS. Experimental results show that our method is superior to the state-of-the-art metrics and achieves an excellent performance.

关键词： no-reference stereoscopic image quality assessment (NR-SIQA) convolution neural network (CNN) human visual system (HVS)

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FSDN: image frequency and semantic decomposition network for image dehazing

FSDN: Image frequency and semantic decomposition network for...

引用

2024 conference on visual communications and image processing

作者： Tong, Zongyang Liu, Mingyu Jin, Xin Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

image dehazing plays a crucial role in autonomous driving and outdoor surveillance. However, as haze affects different components of an image in various ways and degrees, existing methods treat the image as a singular input and overlook the need to decouple different components, leading to mutual interference during the enhancement of each component. Consequently, issues such as insufficient color restoration or blurred edges may arise. In this paper, we introduce a novel tri-branch network for Single image Dehazing that independently extracts low-frequency, high-frequency, and semantic information from images using three distinct sub-networks. A meticulously designed fusion network is then employed to integrate the information from these three branches to produce the final dehazed image. To facilitate the training of such a complex network, we propose a two-stage training approach. Experimental results demonstrate that our approach achieves state-of-the-art (SOTA) performance.

关键词： deep learning dehazing transformer

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DesnowFormer: an effective transformer-based image desnowing network

DesnowFormer: an effective transformer-based image desnowing...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Ting Jiang, Nanfeng Lin, Junhong Lin, Jielian Zhao, Tiesong Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transm Fuzhou Peoples R China

ISBN: (纸本)9781665475921

Single image desnowing is an important and challenge task for lots of computer vision applications, such as visual tracking and video surveillance. Although existing deep learning-based methods have achieved promising results, most of them rely on the local deep features and neglect global relationship information between the local regions. Therefore, inevitably leading to over-smooth or detail loss results. To solve this issue, we design a UNet-based end-to-end architecture for image desnowing. Specially, to better characterize global information and preserve image detail, we combine Window-based Self-Attention (WSA) transformer block with Residue Spatial Attention (RSA) to build basic unit of our network. Besides, to protect the structure of the image effectively, we also introduce a Residue Channel (RC) loss to guide high-quality image restoration. Extensive experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves new state-of-the-art results.

关键词： image Desnowing Transformer block Residual Spatial Attention

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Alignment of image-Text and Video-Text Datasets 31

Alignment of Image-Text and Video-Text Datasets

引用

31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Ozkose, Yunus Emre Gokce, Zeynep Duygulu, Pinar Hacettepe Univ Bilgisayar Muhendisligi Ankara Turkiye

ISBN: (纸本)9798350343557

In this study, the alignment of video-text and image-text 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.

关键词： dataset alignment deep learning machine learning

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Fast Retrieval of Pharmaceutical Packaging images Using Keypoint Matching with Angle and Scale Voting for Outlier Rejection

Fast Retrieval of Pharmaceutical Packaging Images Using Keyp...

引用

2024 conference on visual communications and image processing

作者： Zakaria, Yona Ishiyama, Rui Ishidera, Eiki Matsui, Tomokazu Yasumoto, Keiichi Nara Inst Sci & Technol Nara Japan Univ Dodoma Dodoma Tanzania NEC Corp Ltd Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Counterfeit medicines present a severe public health threat, especially in low-resource countries where consumers lack reliable means to verify the medicines they purchase. visual inspection of medicine packaging images through keypoint matching techniques offers a promising approach for detecting design inconsistencies that could indicate counterfeit products. However, conventional methods often struggle with high computational costs and reduced accuracy when processing images of varying quality and perspectives. To address these limitations, we propose the Angle and Scale Voting (ASVote) method, which enhances keypoint-based image matching by introducing a 2D voting mechanism that leverages relative angles and scales of the keypoints to eliminate false matches(outliers) while identifying consistent matches (inliers). This approach significantly improves both processing time and accuracy. Experiments on a real-world dataset of medicine packages show that ASVote improves processing time and accuracy, outperforming conventional methods.

关键词： keypoint feature matching RANSAC USAC image retrieval visual inspection counterfeit drugs

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Perceptual Evaluation of Pre-processing for Video Transcoding

Perceptual Evaluation of Pre-processing for Video Transcodin...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Huang, Shiyu Luo, Ziyuan Xu, Jiahua Zhou, Wei Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.

关键词： Video quality assessment Subjective evaluation Database Pre-processing for video transcoding

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Complexity scalable motion estimation

Complexity scalable motion estimation

引用

conference on visual communications and image processing 2002

作者： Braspenning, R de Haan, G Hentschel, C Philips Res Labs Eindhoven Netherlands

ISBN: (纸本)0819444111

Complexity scalable algorithms are capable of trading resource usage for output quality in a near-optimal way. We present a complexity scalable motion estimation algorithm based on the 3-D recursive search block matcher. We introduce data prioritizing as a new approach to scalability. With this approach, we achieve a near-constant complexity and a continuous quality-resource distribution. While maintaining acceptable quality, it is possible to vary the resource usage from below 1 match-error calculation per block on the average to more than 5 match-error calculations per block on the average.

关键词： complexity scalability motion estimation scalable video algorithms variable-complexity algorithms quality of service image analysis

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SMRD: A Local Feature Descriptor for Multi-modal image Registration

SMRD: A Local Feature Descriptor for Multi-modal Image Regis...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Xie, Jiayu Jin, Xin Cao, Hongkun Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen Peoples R China

ISBN: (纸本)9781728185514

image registration among multimodality has received increasing attention in the scope of computer vision and computational photography nowadays. However, the nonlinear intensity variations prohibit the accurate feature points matching between modal-different image pairs. Thus, a robust image descriptor for multi-modal image registration is proposed, named shearlet-based modality robust descriptor(SMRD). The anisotropic feature of edge and texture information in multi-scale is encoded to describe the region around a point of interest based on discrete shearlet transform. We conducted the experiments to verify the proposed SMRD compared with several state-of-the-art multi-modal/multispectral descriptors on four different multi-modal datasets. The experimental results showed that our SMRD achieves superior performance than other methods in terms of precision, recall and F1-score.

关键词： Multi-modal image registration feature descriptor discrete shearlet transform

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Lossy Coding for Spatially Adaptive Conditioning in Semantic image Communication

Lossy Coding for Spatially Adaptive Conditioning in Semantic...

引用

2024 conference on visual communications and image processing

作者： Eteke, Cem Griessel, Alexander Kellerer, Wolfgang Steinbach, Eckehard Tech Univ Munich Sch Computat Informat & Technol Chair Media Technol D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Chair Commun Networks D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Munich Inst Robot & Machine Intelligence D-80333 Munich Germany

ISBN: (纸本)9798331529543;9798331529550

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

关键词： semantic communication lossy semantic coding semantic augmentation image synthesis

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