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检索条件"任意字段=2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023"

共 2296 条记录，以下是191-200 订阅

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Multi-information Aggregation Network for Fundus image Quality Assessment

Multi-information Aggregation Network for Fundus Image Quali...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Li, Yuan Yue, Guanghui Duan, Lvyin Wu, Honglv Wang, Tianfu Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen 518060 Peoples R China

ISBN: (纸本)9781665475921

Fundus image quality assessment (IQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists. Existing fundus IQA methods mainly explore local information to consider local distortions from convolutional neural networks (CNNs), yet ignoring global distortions. In this paper, we propose a novel multi-information aggregation network, termed MA-Net, for fundus IQA by extracting both local and global information. Specifically, MA-Net adopts an asymmetric dual-branch structure. For an input image, it uses the ResNet50 and vision transformer (ViT) to obtain the local and global representations from the upper and lower branches, respectively. In addition, MA-Net separately feed different images into the two branches to rank their quality for supplementing the feature representations. Thanks to the exploration of intra- and inter-class information between images, our MA-Net is competent for the fundus IQA task. Experiment results on the EyeQ dataset show that our MA-Net outperforms the baselines (i.e., ResNet50 and ViT) by 3.06% and 7.61% in Acc, and is superior to the mainstream methods.

关键词： fundus image quality assessment multi-task visual transformer convolutional neural network

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LOSSLESS PREDICTIVE CODING WITH BAYESIAN TREATMENT

LOSSLESS PREDICTIVE CODING WITH BAYESIAN TREATMENT

引用

ieee international conference on visual communications and image processing (vcip)

作者： Jing, Liu Yang, Xiaokang Zhai, Guangtao Chen, Li Sun, Xianghui Chen, Wanhong Zuo, Ying Shanghai Jiao Tong Univ Shanghai 200030 Peoples R China China Film Archive Beijing Peoples R China

ISBN: (纸本)9781479902880

Natural image statistics have been widely exploited for lossless predictive coding and other applications. However, traditional adaptive techniques always focus on the local consistency of training set regardless of what the predicted target looks like. We investigate the problem of introducing the model evidence of predicted target since self-similarity inherent in natural images gives some kind of prior information for the distribution of predicted result. The proposed Bayesian model integrated with both training evidence and target evidence takes full advantages of local structure as well as self-similarity. Experimental results demonstrate that the proposed context model achieves best results compared with the state-of-the-art lossless predictors.

关键词： lossless image predictive coding Bayesian method local consistency self-similarity

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Lightweight Color image Demosaicking with Multi-Core Feature Extraction

Lightweight Color Image Demosaicking with Multi-Core Feature...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Tan, Yufei Chang, Kan Li, Hengxin Tang, Zhenhua Qin, Tuanfa Guangxi Univ Sch Comp & Elect Informat Nanning Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning Peoples R China

ISBN: (纸本)9781728180687

Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently r econstruct t he full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine t he i nitial r econstructed i mage. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.

关键词： convolutional neural network image demosaicking feature extraction channel attention residual learning

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Feature Decomposition and Attribute Augmentation for Attribute-based Person Search

Feature Decomposition and Attribute Augmentation for Attribu...

引用

2023 ieee international conference on visual communications and image processing, vcip 2023

作者： Wang, Xin Liu, Fangfang Guo, Caili Li, Zheng Zhang, Hao Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350359855

The attribute-based person search task aims to find matching pedestrian images by text attributes, which is relevant in scenarios where no query image is given. However, the existing methods exhibit inferior performance due to their inadequate representation of local fine-grained features, which hinders their ability to effectively model intra-class variations. In addition, there is a zero-shot retrieval problem due to the large number of unseen categories in the test set, resulting in suboptimal generalization performance. In this paper, we propose a novel Feature Decomposition and Attribute Augmentation (FDAA) framework to solve the above problems. Firstly, by decomposing the global features of the image from different directions, the local features are extracted at multiple granularities, thus effectively improving the discrimination ability of the model. Secondly, an attribute augmentation strategy is proposed that can effectively expand the combination of attributes during training and improve the generalization ability of the model. Extensive experiments on the PETA, Market-1501 Attribute, and PA100K datasets demonstrate the effectiveness of our proposed method, outperforming state-of-the-art methods. © 2023 ieee.

关键词： image enhancement

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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Berthet, Alexandre Dugelay, Jean-Luc Eurecom Dept Digital Secur Biot France

ISBN: (纸本)9781728180687

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields a nd have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Semantics

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MOTION-BASED DEPTH ESTIMATION FOR 2D-TO-3D VIDEO CONVERSION

MOTION-BASED DEPTH ESTIMATION FOR 2D-TO-3D VIDEO CONVERSION

引用

ieee international conference on visual communications and image processing (vcip)

作者： Wang, Ming-Jiun Chen, Chun-Fu Lee, Gwo Giun U.S. Army Research Laboratory MD USA

ISBN: (纸本)9781479902880

This paper presents a motion-based depth estimation algorithm for automatic 2D-to-3D video conversion algorithm by employing the co-occurrence matrix of motion vectors (MVCM). Video scenes possess distinct signatures of MVCM, which enables exploiting the corresponding motion-depth relation for depth generation. The subsequent motion-compensated depth updating scheme provides stable and comfort 3D visual quality as synthesized by depth-image-based rendering. The simulation results of several high-definition image sequences indicate that the proposed algorithm produces better and more reasonable depth than two motion-based depth estimation algorithms. With the adaptive depth estimation scheme using MVCM, the proposed 2D-to-3D video conversion algorithm can accommodate a great variety of visual contents. It thus provides an efficient and reliable solution towards the problem of automatic 3D video content creation.

关键词： Depth estimation motion estimation co-occurrence matrix depth-image-based-rendering(DIBR)

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LMM-driven Semantic image-Text Coding for Ultra Low-bitrate Learned image Compression

LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate ...

引用

2024 conference on visual communications and image processing

作者： Murai, Shimon Sun, Heming Katto, Jiro Waseda Univ Sch Fundamental Sci & Engn Tokyo Japan Yokohama Natl Univ Fac Engn Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior perceptual quality by using image captions as sub-information. This paper demonstrates that using a large multi-modal model (LMM), it is possible to generate captions and compress them within a single model. We also propose a novel semantic-perceptual-oriented fine-tuning method applicable to any LIC network, resulting in a 41.58% improvement in LPIPS BD-rate compared to existing methods. Our implementation and pre-trained weights are available at https://***/tokkiwa/imageTextCoding.

关键词： Learned image Compression (LIC) Large Multi-modal Model (LMM) Latent Diffusion Model (LDM)

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HDR image Compression with Convolutional Autoencoder

HDR Image Compression with Convolutional Autoencoder

引用

ieee international conference on visual communications and image processing (vcip)

作者： Han, Fei Wang, Jin Xiong, Ruiqin Zhu, Qing Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China

ISBN: (纸本)9781728180687

As one of the next- generation multimedia technology, high dynamic range (HDR) imaging technology has been widely applied. Due to its wider color range, HDR image brings greater compression and storage burden compared with traditional LDR image. To solve this problem, in this paper, a two-layer HDR image compression framework based on convolutional neural networks is proposed. The framework is composed of a base layer which provides backward compatibility with the standard JPEG, and an extension layer based on a convolutional variational autoencoder neural networks and a post-processing module. The autoencoder mainly includes a nonlinear transform encoder, a binarized quantizer and a nonlinear transform decoder. Compared with traditional codecs, the proposed CNN autoencoder is more flexible and can retain more image semantic information, which will improve the quality of decoded HDR image. Moreover, to reduce the compression artifacts and noise of reconstructed HDR image, a post-processing method based on group convolutional neural networks is designed. Experimental results show that our method outperforms JPEG XT profile A, B, C and other methods in terms of HDR-VDP-2 evaluation metric. Meanwhile, our scheme also provides backward compatibility with the standard JPEG.

关键词： Convolutional neural networks

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ROTATIONAL BASED REWRITABLE DATA HIDING IN JPEG

ROTATIONAL BASED REWRITABLE DATA HIDING IN JPEG

引用

ieee international conference on visual communications and image processing (vcip)

作者： Ong, SimYing Wong, KokSheik Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781479902880

This paper proposes a novel rotational method on AC coefficient pairs to embed data into a JPEG compressed image. The purpose is to improve the carrier capacity of [1] while maintaing its original feature in controlling quality degradation. The proposed method exploits two properties in the quantized AC coefficients, namely, large magnitude and short run of zeros for low frequency subbands, and vice versa. The coefficients are first grouped into pairs and then rotated to the left or right directions to create distinctive states, where each can be utilized to represent external data. The AC coefficients are not modified and no additional AC coefficients are introduced for data embedding. However, preprocessing is needed so that all blocks satisfy the properties assumed to ensure correct data extraction and image recovery. The proposed method is rewritable because the host image can be re-utilized without causing further distortion. Experiments were conducted to verify the basic performance of the proposed method. On average, the proposed method is able to embed up to similar to 9318 bits in the test images of quality factor 80.

关键词： Zero run value rotation rewritable data hiding JPEG compressed image DCT

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SHAKING VIDEO SYNTHESIS FOR VIDEO STABILIZATION PERFORMANCE ASSESSMENT

SHAKING VIDEO SYNTHESIS FOR VIDEO STABILIZATION PERFORMANCE ...

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ieee international conference on visual communications and image processing (vcip)

作者： Qu, Hui Song, Li Xue, Gengjian Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200030 Peoples R China

ISBN: (纸本)9781479902880

The goal of video stabilization is to remove the unwanted camera motion and obtain stable versions. Theoretically, a good stabilization algorithm should remove the unwanted motion without the loss of image qualities. However, due to the lack of ground-truth video frames, the accurate performance evaluation of different algorithms is hard. Most existing evaluation techniques usually synthesize stable videos from shaking ones, but they are not effective enough. Different from previous methods, in this paper we propose a novel method which synthesize shaking videos from stable frames. Based on the synthetic shaking videos, we perform preliminary video stabilization performance assessment on three stabilization algorithms. Our shaking video synthesis method can not only give a benchmark for full-reference video stabilization performance assessment, but also provide a basis for exploring the theoretical bound of video stabilization which may help to improve existing stabilization algorithms.

关键词： Video stabilization ground-truth performance assessment

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