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

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Asymmetric Stereoscopic Video Encoding Algorithm Based On Subjective visual Characteristic

Asymmetric Stereoscopic Video Encoding Algorithm Based On Su...

引用

International conference on Wireless communications and Signal processing

作者： Liu, Shu Liu, Feng Fan, Jingjing Xia, Hongfei Nanjing Univ Posts & Telecommun Image Proc & Image Commun Jiangsu Key Lab Nanjing Peoples R China ZTE Telecom Co Ltd Nanjing Peoples R China

ISBN: (纸本)9781424448562

In traditional asymmetric stereo video encoding scheme, one eye is represented with high quality sequence, the other eye is represented with lower quality one. However, if the low quality view is the observer's dominant eye, the masking effect will not work. Based on this human visual characteristic, this paper proposed a GOP-based resolution cross-switching asymmetric encoding scheme. By allocating degradation to both of views in a balanced way over time, our experimental results show better compression efficiency than JMVM reference software and better subjective visual quality than the traditional asymmetric stereo video encoding scheme. Our stereo video coding scheme can be a trade-off between compression performance and subjective visual quality.

关键词： stereoscopic video subjective visual characteristic asymmetric encoding GOP-based resolution cross-switch

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ISDN AUDIO COLOR-GRAPHICS TELECONFERENCING SYSTEM

ISDN AUDIO COLOR-GRAPHICS TELECONFERENCING SYSTEM

引用

CONF ON visual communications AND image processing : image processing

作者： OYAIZU, I TANAKA, K YAMAGUCHI, T MIYABO, K TAKAHASHI, M NTT Human Interface Labs. (Japan)

ISBN: (纸本)0819407437

An audio-graphic teleconferencing system has been developed that uses ordinary personal computers (PCs) interconnected over a basic rate (2B+D) ISDN line. The system supports high-speed transmission of 200-dpi resolution documents read by an optical scanner and presented on the displays of the conference participants. While looking at the same material, the conferees can interactively converse and make handwritten notations for all the participants to see on the document via a LCD tablet. This paper describes the configuration and performance of the system, focusing mainly on the ISDN-based multi-media transmission method and the method of reducing and enlarging binary images.

关键词： Signal processing image processing Tablets Data communications Data conversion LCDs Computing systems visual communications Telecommunications Data modeling

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Patch-Based image Deblocking Using Geodesic Distance Weighted Low-Rank Approximation

Patch-Based Image Deblocking Using Geodesic Distance Weighte...

引用

IEEE visual communications and image processing conference

作者： Li, Mading Liu, Jiaying Ren, Jie Guo, Zongming Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China

ISBN: (纸本)9781479961399

Transform coding based on the discrete cosine transform (DCT) has been widely used in image coding standards. However, the coded images often suffer from severe visual distortions such as blocking artifacts. In this paper, we propose a novel image deblocking method to address the blocking artifacts reduction problem in a patch-based scheme. image patches are clustered and reconstructed by the low-rank approximation, which is weighted by the geodesic distance. Experimental results show that the proposed method achieves higher PSNR than the state-of-the-art deblocking and denoising methods and the processed images present good visual quality.

关键词： image deblocking patch-based low-rank approximation geodesic distance reconstruction

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An Efficient Vision Transformer Model for PCB Component Classification 31

An Efficient Vision Transformer Model for PCB Component Clas...

引用

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

作者： Surmeli, Cagri Can Ekenel, Hazim Kemal Istanbul Tech Univ ASELSAN Inc Istanbul Turkiye Istanbul Tech Univ Istanbul Turkiye

ISBN: (纸本)9798350343557

Printed circuit board (PCB) assemblies in everyday electronic devices are mass-produced. As a result of this production volume, a fast way of visual inspection is necessary. An integral part of visual inspection systems is PCB component classification. In this paper, we have explored use of the Vision Transformer (ViT), which is a recent state-of-the-art image classification approach, for PCB component classification. We have employed several ViT models that are available in the literature and also proposed a new compact, efficient, and high performing ViT model, named as ViT-Mini. We have conducted extensive experiments on the FICS-PCB dataset in order to comparatively evaluate the ViT models' performance. The highest achieved accuracy is 99.46% for capacitor and resistor classification and 96.52% for classification of capacitor, resistor, inductor, transistor, diode, and IC. The proposed compact model's performance is comparable with the ones obtained with larger models, which indicates its suitability for real-time applications.

关键词： PCB Components image Classification Vision Transformers

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Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

引用

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

作者： Zhang, Yichi Liu, Hengyu Ding, Dandan Ma, Zhan Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665475921

Compared to RGB images non-linearly mapped from RAW data through the image Signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Computational photography low-light transformer fast Fourier convolution

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Using Regularity Unit As Guidance For Summarization-Based image Resizing

Using Regularity Unit As Guidance For Summarization-Based Im...

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

作者： Hsiao, Fang-Tsung Lin, Yi-Hsien Lu, Yi-Chang Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 10617 Taiwan

ISBN: (纸本)9781728185514

In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.

关键词： image resizing image summarization repeating pattern removal

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DEPTH FROM DEFOCUS AND BLUR FOR SINGLE image

DEPTH FROM DEFOCUS AND BLUR FOR SINGLE IMAGE

引用

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

作者： Sun, Huadong Zhao, Zhijie Jin, Xuesong Niu, Lianding Zhang, Lizhi Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China

ISBN: (纸本)9781479902880

Depth for single image is a hot problem in computer vision, which is very important to 2D/3D image conversion. Generally, depth of the object in the scene varies with the amount of blur in the defocus images. So, depth in the scene can be recovered by measuring the blur. In this paper, a new method for depth estimation based on focus/defocus cue is presented, where the entropy of high frequency subband of wavelet decomposition is regarded as the measure of blur. The proposed method, which is unnecessary to select threshold, can provide pixel-level depth map. The experimental results show that this method is effective and reliable.

关键词： Depth focus/defocus cue measure of blur entropy of high frequency subband

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DISTINGUISHING COMPUTER-GENERATED imageS FROM PHOTOGRAPHIC imageS: A TEXTURE-AWARE DEEP LEARNING-BASED METHOD

DISTINGUISHING COMPUTER-GENERATED IMAGES FROM PHOTOGRAPHIC I...

引用

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

作者： Zhang, Zicheng Sun, Wei Min, Xiongkuo Wang, Tao Lu, Wei Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665475921

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.

关键词： Computer-generated photographic image classification dual-stream neural network texture map

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A Robust Multilinear Mixing Model with l2,1 norm for Unmixing Hyperspectral images

A Robust Multilinear Mixing Model with l2,1 norm for Unmixin...

引用

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

作者： Li, Minglei Zhu, Fei Guo, Alan J. X. Tianjin Univ Ctr Appl Math Tianjin Peoples R China

ISBN: (纸本)9781728180687

The unmixing of hyperspectral data is a hot topic in the field of r emote s ensing. H owever, in p resence o f various types of noise, especially the noisy channels, the performance of unmixing approaches is seriously deteriorated. To enhance the robustness of the unmixing method is a subject worth studying. This paper presents a robust unmixing method based on the recently- proposed multilinear mixing model, where the l(2,1) norm is adopted in the loss function to suppress the influence of noise. The sparseness of abundance is also considered to improve the parameter estimation. The resulting optimization problem is solved by the alternating direction multiplier method (ADMM). Experiments on both synthetic and real images demonstrate the performance of the proposed unmixing strategy.

关键词： hyperspectral image nonlinear unmixing alternating direction method of multipliers robust l(2,1) norm

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ML-FDA: Meta-Learning via Feature Distribution Alignment for Few-Shot Learning

ML-FDA: Meta-Learning via Feature Distribution Alignment for...

引用

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

作者： Li, Yuewen Liu, Binghao Lyu, Shuchang Chen, Lijiang Zhao, Qi Feng, Wenquan Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781665475921

Computer vision tasks suffer from the high cost of collecting large amounts of labeled data. Few-shot Learning (FSL) is a dominant approach to solve this problem because it provides an insight to learn the knowledge of novel categories with few training samples. In FSL task, Meta-learning and metric learning have achieved impressive results. However, the performance of this task is still limited by large intra-class variance and small inter-class distance caused by limited number of few samples. To solve this problem, In this paper, we propose a new method, which integrates meta-learning and metric learning techniques. Specifically, we first propose a feature representation module (FR) to construct representative support class prototypes and query features. Then, we design bias loss to minimize the bias between support and query samples. Furthermore, we design an intra-class loss to minimize the distance between query class prototype and each query sample. We denote this model as ML-FDA and validate it on standard few-shot classification benchmark datasets (MiniimageNet, CIFAR-FS, FC100). The results show that our method improves the performance over other same paradigm methods and achieves the best performance on most benchmarks. The ablation study and visulization analysis also demonstrate the effectiveness of our method.

关键词： Query processing

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