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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91-100 订阅

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MAiVAR: Multimodal Audio-image and Video Action Recognizer

MAiVAR: Multimodal Audio-Image and Video Action Recognizer

引用

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

作者： Shaikh, Muhammad Bilal Chai, Douglas Islam, Syed Mohammed Shamsul Akhtar, Naveed Edith Cowan Univ 270 Joondalup Dr Perth WA 6027 Australia Univ Western Australia 35 Stirling Highway Perth WA 6009 Australia

ISBN: (纸本)9781665475921

Currently, action recognition is predominately performed on video data as processed by CNNs. We investigate if the representation process of CNNs can also be leveraged for multimodal action recognition by incorporating image-based audio representations of actions in a task. To this end, we propose Multimodal Audio-image and Video Action Recognizer (MAiVAR), a CNN-based audio-image to video fusion model that accounts for video and audio modalities to achieve superior action recognition performance. MAiVAR extracts meaningful image representations of audio and fuses it with video representation to achieve better performance as compared to both modalities individually on a large-scale action recognition dataset.

关键词： Large dataset

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Optimized MobileNetV2 Based on Model Pruning for image Classification

Optimized MobileNetV2 Based on Model Pruning for Image Class...

引用

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

作者： Xiao, Peng Pang, Yuliang Feng, Hao Hao, Yu Xian Univ Posts & Telecommunicat Ctr Image & Informat Proc (CIIP) Xian Peoples R China

ISBN: (纸本)9781665475921

Due to the large memory requirement and a large amount of computation, traditional deep learning networks cannot run on mobile devices as well as embedded devices. In this paper, we propose a new mobile architecture combining MobileNetV2 and pruning, which further decreases the Flops and number of parameters. The performance of MobileNetV2 has been widely demonstrated, and pruning operation can not only allow further model compression but also prevent overfitting. We have done ablation experiments at CIIP Tire Data for different pruning combinations. In addition, we introduced a global hyperparameter to effectively weigh the accuracy and precision. Experiments show that the accuracy of 98.3 % is maintained under the premise that the model size is only 804.5 KB, showing better performance than the baseline method.

关键词： model compression MobileNetV2 pruning

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Fidelity-preserving Learning-Based image Compression: Loss Function and Subjective Evaluation Methodology

Fidelity-preserving Learning-Based Image Compression: Loss F...

引用

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

作者： Mohammadi, Shima Wu, Yaojun Ascenso, Joao Instituto de Telecomunicações Instituto Superior Técnico Portugal Bytedance China

ISBN: (纸本)9798350359855

Learning-based image compression methods have emerged as state-of-The-Art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion between the decoded and the original ground truth images. This paper focuses on perceptual optimization of learning-based image compression solutions and proposes: i) novel loss function to be used during training and ii) novel subjective test methodology that aims to evaluate the decoded image fidelity. According to experimental results from the subjective test taken with the new methodology, the optimization procedure can enhance image quality for low-rates while offering no advantage for high-rates. © 2023 ieee.

关键词： image compression

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Block Importance Mapping for Video Encoding

Block Importance Mapping for Video Encoding

引用

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

作者： Enhorn, Jack Hollmann, Christopher Sjoberg, Rickard Strom, Jacob Wennersten, Per Visual Technol Ericsson AB Ericsson Res Kista Sweden

ISBN: (纸本)9781665475921

This paper presents a novel video encoding algorithm called Block Importance Mapping. In this method, blocks are assigned a quantization parameter, QP, offset based on how likely the block samples are to be used as references in encoding of nearby pictures. The reusability estimation is based on a motion search in pictures immediately before and after the current picture in output order. Blocks that are likely to be used as reference blocks are coded with a lower QP value, i.e., higher quality, whereas blocks that are deemed unlikely to be referenced are coded with a higher QP value. This method has been implemented in the VVC reference software VTM and tested on various configurations and contents. It is reported to provide BD-rate savings of 1.93% for PSNR and 3.69% for MS-SSIM in Random Access and 2.26% for PSNR and 4.29% for MS-SSIM in Low Delay coding. For HDR content, wPSNR BD-rate savings of 1.18% in Random Access configuration is reported.

关键词： Video Coding VVC VTM Adaptive QP

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DepthUNet: A Dehazing Model with Adaptive Depth Attention for Natural images

DepthUNet: A Dehazing Model with Adaptive Depth Attention fo...

引用

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

作者： Wang, Kai-Yu Lin, Yu-Hsiang Hsiung, Pao-Ann National Chung Cheng University Department of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350359855

Most image dehazing deep learning models target synthetic datasets of hazy images, resulting in not considering features in natural hazy images. Leveraging on depth attention with adaptation, we propose a novel dehazing network called depthUNet, that is focused on natural images. Utilizing the correlation between depth information and haze distribution, our network enhances its generalization performance on natural images. Furthermore, our method improves the PSNR for the non-homogeneous realistic haze dataset NH-HAZE from 20.66 (DeHamer's result) to 20.74, using only 1.6% of the parameters. Similarly, for the outdoor scenes realistic haze dataset O-HAZE, our method enhances the PSNR from 24.36 (MSBDN's result) to 25.77, with just 6% of the parameters. On natural road images with haze from dataset RTTS, our method improved the vehicle detection rate by 10% in terms of the R-squared value. In summary, our method outperforms all state-of-The-Art methods while utilizing the least number of parameters. © 2023 ieee.

关键词： image enhancement

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image Coding for Machines based on Non-Uniform Importance Allocation

Image Coding for Machines based on Non-Uniform Importance Al...

引用

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

作者： Qi, Yunpeng Feng, Ruoyu Zhang, Zhizheng Chen, Zhibo University of Science and Technology of China Hefei China Microsoft Research Beijing China

ISBN: (纸本)9798350359855

In the Internet era, the explosive growth of media data processing poses significant challenges for the research of image Coding for Machines (ICM) in improving the efficiency of AI models while reducing the burdens of data storage and transmission. Existing ICM methods face challenges in achieving sufficient generalization ability when developing a single codec to handle diverse downstream tasks. To address these issues, we propose a unified ICM framework that facilitates diverse downstream tasks with a novel importance allocation mechanism. Equipped with a spatially variable-rate image compression codec, we introduce two options: online updating and offline predicting the non-uniform quality map, which governs the quality distribution of reconstructed images based on specific downstream tasks. Our proposed method is rigorously evaluated through extensive experiments on diverse and comprehensive fine-grained image classification datasets. The experiment results conclusively demonstrate the effectiveness of the proposed method in achieving a superior rate-distortion trade-off for ICM. © 2023 ieee.

关键词： image coding

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Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized image Compression

Spatial-Channel Context-Based Entropy Modeling for End-to-en...

引用

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

作者： Li, Chongxin Luo, Jixiang Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728180687

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.

关键词： End-to-end optimized image compression context-based entropy coding artificial neural networks

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Context-Aware Seam Restoration for image Extension

Context-Aware Seam Restoration for Image Extension

引用

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

作者： Heo, Yuk Koh, Yeong Jun Kim, Chang-Su Korea University Seoul Korea Republic of Chungnam National University Daejeon Korea Republic of

ISBN: (纸本)9798350359855

A seam is a set of pixels with minimum energy forming a continuous line in an image. By eliminating or duplicating seams iteratively, an input image can be retargeted. However, this process often results in blurring, stretching, or distortion problems around the seams, especially when extending a target image. We propose a novel approach for image extension using content-Aware seam restoration to solve this problem. First, we design CSR-Net, which employs features from the horizontal region of target pixels to restore the seams. Second, we develop an image extension scenario based on the seam restoration and the training methodology of CSR-Net. Experimental results demonstrate that the proposed algorithm provides more accurate expanded results at seam pixels the seams than conventional algorithms. © 2023 ieee.

关键词： Restoration

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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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