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检索条件"任意字段=2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020"

共 1999 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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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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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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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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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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