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检索条件"任意字段=2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014"

共 2408 条记录，以下是61-70 订阅

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No-Reference Stereoscopic image Quality Assessment Based on The visual Pathway of Human visual System

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

引用

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

作者： Meng, Fan Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full of challenges due to the complex relationship between binocular views. In this article, firstly, convolutional neural network (CNN) based on the visual pathway of human visual system (HVS) is built, which simulates different parts of visual pathway such as the optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. Secondly, the two pathways of our method simulate the 'what' and 'where' visual pathway respectively, which are endowed with different feature extraction capabilities. Finally, we find a different application way for 3D-convolution, employing it fuse the information from left and right view, rather than just extracting temporal features in video. The experimental results show that our proposed method is more in line with subjective score and has good generalization.

关键词： Stereoscopic image quality assessment (SIQA) Convolutional neural network (CNN) the human visual system (HVS) 3D-convolution (C3D)

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Learning from the NN-based Compressed Domain with Deep Feature Reconstruction Loss

Learning from the NN-based Compressed Domain with Deep Featu...

引用

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

作者： Chen, Liuhong Sun, Heming Zeng, Xiaoyang Fan, Yibo Fudan Univ Shanghai Peoples R China Waseda Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665475921

To speedup the image classification process which conventionally takes the reconstructed images as input, compressed domain methods choose to use the compressed images without decompression as input. Correspondingly, there will be a certain decline about the accuracy. Our goal in this paper is to raise the accuracy of compressed domain classification method using compressed images output by the NN-based image compression networks. Firstly, we design a hybrid objective loss function which contains the reconstruction loss of deep feature map. Secondly, one image reconstruction layer is integrated into the image classification network for up-sampling the compressed representation. These methods greatly help increase the compressed domain image classification accuracy and need no extra computational complexity. Experimental results on the benchmark imageNet prove that our design outperforms the latest work ResNet-41 with a large accuracy gain, about 4.49% on the top-1 classification accuracy. Besides, the accuracy lagging behinds the method using reconstructed images is also reduced to 0.47%. Moreover, our designed classification network has the lowest computational complexity and model complexity.

关键词： Compressed domain image analysis image classification LIC (Learned image Compression) feature reconstruction

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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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CROSS-COMPONENT SAMPLE OFFSET FOR image AND VIDEO CODING

CROSS-COMPONENT SAMPLE OFFSET FOR IMAGE AND VIDEO CODING

引用

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

作者： Du, Yixin Zhao, Xin Liu, Shan Tencent Amer 2747 Pk Blvd Palo Alto CA 94306 USA

ISBN: (纸本)9781728185514

Existing cross-component video coding technologies have shown great potential on improving coding efficiency. The fundamental insight of cross-component coding technology is respecting the statistical correlations among different color components. In this paper, a Cross-Component Sample Offset (CCSO) approach for image and video coding is proposed inspired by the observation that, luma component tends to contain more texture, while chroma component is relatively smoother. The key component of CCSO is a nonlinear offset mapping mechanism implemented as a look-up-table (LUT). The input of the mapping is the co-located reconstructed samples of luma component, and the output is offset values applied on chroma component. The proposed method has been implemented on top of a recent version of libaom. Experimental results show that the proposed approach brings 1.16% Random Access (RA) BD-rate saving on top of AV1 with marginal encoding/decoding time increase.

关键词： Cross-component prediction video coding AV1 loop filtering

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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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Improving Latent Quantization of Learned image Compression with Gradient Scaling

Improving Latent Quantization of Learned Image Compression w...

引用

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

作者： Sun, Heming Yu, Lu Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan Zhejiang Univ Inst Informat & Commun Engn Hangzhou Peoples R China JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9781665475921

Learned image compression (LIC) has shown its superior compression ability. Quantization is an inevitable stage to generate quantized latent for the entropy coding. To solve the non-differentiable problem of quantization in the training phase, many differentiable approximated quantization methods have been proposed. However, the derivative of quantized latent to non-quantized latent are set as one in most of the previous methods. As a result, the quantization error between non-quantized and quantized latent is not taken into consideration in the gradient descent. To address this issue, we exploit the gradient scaling method to scale the gradient of non-quantized latent in the back-propagation. The experimental results show that we can outperform the recent LIC quantization methods.

关键词： Learned image compression Quantization Gradient scaling

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A Novel image Quality Assessment based on an Adaptive Feature for image Characteristics and Distortion Types

A Novel Image Quality Assessment based on an Adaptive Featur...

引用

ieee visual communications and image processing (vcip) conference

作者： Bae, Sung-Ho Kim, Munchurl Korea Adv Inst Sci & Technol Sch Elect Engn 291 Daehak Ro Daejeon 305701 South Korea

ISBN: (纸本)9781467373142

In this paper, we reveal that many conventional features used in computational image quality assessment (IQA) methods can hardly characterize perceived distortions on various image characteristics and distortion types, thus resulting in relatively low prediction performance of visual quality scores. To solve this problem, we propose a new IQA method, called Structural Contrast-Quality Index (SC-QI) which is based on structural contrast index (SCI) as a very effective feature. SCI can adaptively quantify perceived distortions depending on various image characteristics and distortions types. In addition to SCI, some other perceptually important features that reflect effects of contrast sensitivity function and chrominance component variation are also combined into the proposed SC-QI. Our comprehensive experiments on three large IQA datasets verify that the proposed SC-QI outperforms the state-of-the-art ones while accompanying lower computational complexity.

关键词： human visual system (HVS) image quality assessment (IQA) structural contrast index (SCI)

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Tire Pattern image Classification using Variational Auto-Encoder with Contrastive Learning

Tire Pattern Image Classification using Variational Auto-Enc...

引用

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

作者： Yang, Jianning Xue, Jiahao Feng, Xiaodong Song, Chaoqi Hao, Yu Xian Univ Posts & Telecommunicat Sch Communicat & Informat Engn Xian Peoples R China

ISBN: (纸本)9781665475921

Tire pattern image classification is an important computer vision problem in pubic security, which can guide policeman to detect criminal cases. It remains challenge due to the small diversity within different classes. Generally, a tire pattern image classification system may require two characteristics: high accuracy and low computation. In this paper, we first assume that capturing rich feature representation will benefits tire classification and learning through a lightweight network will improve computing efficiency. We then propose a simple yet efficient two-stage training mechanism: 1) We learn a feature extractor using a Variational Auto-Encoder framework constrained by contrastive learning, projecting images to latent space owing rich feature representation. 2) We train a single-layer linear classification network depend on the features extracted by the previous trained encoder. The Top-1 and Top-5 accuracy on tire pattern dataset is 89.8% and 96.6% respectively, validating the effectiveness of our strategy.

关键词： Tire Pattern image classification Variational Auto-Encoder Contrastive Learning

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Video Compression based on Jointly Learned Down-Sampling and Super-Resolution Networks

Video Compression based on Jointly Learned Down-Sampling and...

引用

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

作者： Wei, Yuzhuo Chen, Li Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

With the blooming of deep learning technology in computer vision, the integration of deep learning and the traditional video coding has made significant improvements, especially applying the super-resolution neural network as the post-processing module in the down-sampling-based video compression framework. However, the pre-processing module lacks back-propagated gradients for jointly considering down-sampling and up-sampling due to the non-differentiability of the traditional video codec. In this paper, we propose an end-to-end down-sampling-based video compression framework applying convolutional neural networks both as down-sampling and up-sampling. We use a virtual codec neural network to approximate the actual video codec so that the gradient can be effectively back-propagated for joint training. Experimental results show the superiority of our proposed framework compared with the predefined down-sampling-based video compression and various methods of joint training.

关键词： super-resolution video compression deep learning

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The Enhancement of Underexposed images with Blurred Reflectance

The Enhancement of Underexposed Images with Blurred Reflecta...

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

作者： Zhou, Jinchao Wan, Renjie Li, Haoliang Kot, Alex C. Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728180687

The images captured in the low-light conditions always suffer from low visibility. Enhancing the visibility of the low-light image is of broad application to various computer vision tasks. Based on the classical Retinex model, previous methods assume the reflectance components as a well-exposed image. In this paper, we introduce the blurring distortion into the Retinex model to cover more general and challenging scenarios. We further propose a two-stage framework to extract the reflectance images and remove the blurring distortion separately. Specifically, we optimize the whole network by embedding a mechanism robust to the pixel misalignment in the training dataset. The experimental results show that our proposed method achieves promising results.

关键词： Low-light image blur illumination reflectance

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