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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 2298 条记录，以下是101-110 订阅

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Underwater image Enhancement with Multi-Scale Residual Attention Network

Underwater Image Enhancement with Multi-Scale Residual Atten...

引用

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

作者： Ueki, Yosuke Ikehara, Masaaki Keio Univ EEE Dept Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781728185514

Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.

关键词： image processing image enhancement image restoration underwater image attention

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EDGE-PRESERVING SINGLE DEPTH image INTERPOLATION

EDGE-PRESERVING SINGLE DEPTH IMAGE INTERPOLATION

引用

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

作者： Zhong, Gang Yu, Li Zhou, Peng Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781479902880

Depth image upsampling is an important issue in three-dimensional (3D) applications. However, edge blurring artifacts are still challenging problems in depth image upsampling, resulting in jagged artifacts in synthesized views which produce unpleasant visual perception. In this paper, an edge-preserving single depth image interpolation (ESDI) method is proposed. Specifically, local planar hypothesis (LPH) assuming that depth in natural scene are clustered as local planar planes is first explored. Then finite candidates generation (FCG) is proposed to generate limited discrete values satisfied with LPH to interpolated pixels. At last, the optimal combination of candidates is formulated as an energy minimization problem with a constraint in gradient domain, solved by iterated conditional modes (ICM) algorithm. Experiments demonstrate that ESDI achieves high resolution (HR) depth image with clear and sharp edges, and produces synthesized views with desirable quality.

关键词： Depth image interpolation local planar hypothesis gradient profile prior gradient domain

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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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STVP: A Spatiotemporal visual Perception Method for User-generated Content Video Quality Assessment

STVP: A Spatiotemporal Visual Perception Method for User-gen...

引用

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

作者： Ai, Da Lu, Mingyue Wang, Jiahao Liu, Ying Xi'An University of Posts and Telecommunications Center for Image and Information Processing Xi'an China Xi'An University of Posts and Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350359855

With the popularity and development of short video applications, the behavior of using mobile devices to shoot and share user-generated content (UGC) videos has become increasingly common. Video quality assessment (VQA) is critical in guaranteeing end-user viewing experiences. UGC-VQA is a challenging problem due to the complexity and variety of distortion types of UGC videos and the absence of reference videos. To improve the consistency of UGC-VQA results and human subjective ratings, in this paper, we propose a UGC-VQA method based on spatiotemporal visual perception (STVP). Firstly, a hierarchical feature fusion module was added to the feature extraction network to realize the fusion of low-level visual features and high-level semantic features, and obtain the quality perception features with rich visual information. Then, we use the self-Attention to weight different frames to distinguish their importance. The long short-Term memory (LSTM) network and the time pool are used to model long-Term dependencies and temporal memory effects. Experimental results on UGC-VQA datasets show that the proposed method achieves a performance improvement of nearly 2%, and its evaluation results are more consistent with human visual perception. © 2023 ieee.

关键词： Semantics

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Improving Latent Quantization of Learned image Compression with Gradient Scaling

Improving Latent Quantization of Learned Image Compression w...

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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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MULTI-MODEL PREDICTION FOR image SET COMPRESSION

MULTI-MODEL PREDICTION FOR IMAGE SET COMPRESSION

引用

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

作者： Shi, Zhongbo Sun, Xiaoyan Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781479902880

The key task in image set compression is how to efficiently remove set redundancy among images and within a single image. In this paper, we propose the first multi-model prediction (MoP) method for image set compression to significantly reduce inter image redundancy. Unlike the previous prediction methods, our MoP enhances the correlation between images using feature-based geometric multi-model fitting. Based on estimated geometric models, multiple deformed prediction images are generated to reduce geometric distortions in different image regions. The block-based adaptive motion compensation is then adopted to further eliminate local variances. Experimental results demonstrate the advantage of our approach, especially for images with complicated scenes and geometric relationships.

关键词： image set compression multiple models prediction HEVC

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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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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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Frequency-Aware Hierarchical image Compression for Humans and Machines

Frequency-Aware Hierarchical Image Compression for Humans an...

引用

2024 conference on visual communications and image processing

作者： Luo, Yue Zhang, Zixiang Kuang, Jinhao Yu, Li Huazhong Univ Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

To achieve efficient compression for both human vision and machine perception, scalable coding methods have been proposed in recent years. However, existing methods do not fully eliminate the redundancy between features corresponding to different tasks, resulting in suboptimal coding performance. In this paper, we propose a frequency-aware hierarchical image compression framework designed for humans and machines. Specifically, we investigate task relationships from a frequency perspective, utilizing only HF information for machine vision tasks and leveraging both HF and LF features for image reconstruction. Besides, the residual block embedded octave convolution module is designed to enhance the information interaction between HF features and LF features. Additionally, a dual-frequency channel-wise entropy model is applied to reasonably exploit the correlation between different tasks, thereby improving multi-task performance. The experiments show that the proposed method offers -69.3%similar to-75.3% coding gains on machine vision tasks compared to the relevant benchmarks, and -19.1% gains over state-of-the-art scalable image codec in terms of image reconstruction quality.

关键词： scalable image coding image coding for machines learned image compression

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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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