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共 2409 条记录，以下是151-160 订阅

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Real-time Air Quality Estimation Based on Color image processing

Real-time Air Quality Estimation Based on Color Image Proces...

引用

ieee visual communications and image processing conference

作者： Wang, Haoqian Yuan, Xin Wang, Xingzheng Zhang, Yongbing Dai, Qionghai Shenzhen Key Lab Broadband Network & Multimedia Shenzhen Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781479961399

This paper address the problem of efficient, realtime estimation of the particulate mass concentration, exactly PM2.5 (particles with aerodynamic diameters less than 2.5 mu m) from a superb view image. And the proposed method is to achieve high degree of accuracy at the cost of only modest user's effort by analyzing the relationship between the PM2.5 and the degradation of the observed image. With the fitting algorithm with experimental data, the PM2.5 could be real-time estimated by a general camera with little artificial participation, and the correlation coefficient produced by our data set and the standard observation will be as high as 0.8219, as the MSE (Mean Squared Error) value 51.2324 (mu g)/(3)(m).

关键词： image process PM2.5 dark point light extinction

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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

引用

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

作者： Berthet, Alexandre Dugelay, Jean-Luc Eurecom Dept Digital Secur Biot France

ISBN: (纸本)9781728180687

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields a nd have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Semantics

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See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy image

See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recove...

引用

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

作者： Wang, Ping Wu, Wei Li, Zhu Liu, Yong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO USA

ISBN: (纸本)9781728185514

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.

关键词： SIFT key point recovery rainy image extrema detection and description deep learning

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Lossy Coding for Spatially Adaptive Conditioning in Semantic image Communication

Lossy Coding for Spatially Adaptive Conditioning in Semantic...

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2024 conference on visual communications and image processing

作者： Eteke, Cem Griessel, Alexander Kellerer, Wolfgang Steinbach, Eckehard Tech Univ Munich Sch Computat Informat & Technol Chair Media Technol D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Chair Commun Networks D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol Munich Inst Robot & Machine Intelligence D-80333 Munich Germany

ISBN: (纸本)9798331529543;9798331529550

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

关键词： semantic communication lossy semantic coding semantic augmentation image synthesis

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FAST MERGE MODE DECISION FOR DIAMOND SEARCH IN HIGH EFFICIENCY VIDEO CODING

FAST MERGE MODE DECISION FOR DIAMOND SEARCH IN HIGH EFFICIEN...

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

作者： Kim, Miok Lee, Hyuk-Jae Ling, Nam Santa Clara Univ Dept Comp Engn Santa Clara CA 95053 USA Seoul Natl Univ Dept Elect Engn Seoul 151742 South Korea

ISBN: (纸本)9781479902880

This paper proposes a merge mode decision algorithm while maintaining the accuracy of a diamond search (DS) in motion estimation and compensation. In High Efficiency Video Coding (HEVC), the merge mode is used to reduce the bit rate in order to carry motion information. The rate-distortion (RD) cost of the merge mode is compared with the RD cost of the inter-prediction mode in the course of the motion estimation which can be terminated early when the merge cost is smaller than the estimated cost of the inter-prediction mode. To this end, this paper proposes a fast merge mode prediction algorithm when the DS is used for motion estimation. The main purpose of this work is to estimate the RD cost of the merge mode in advance by utilizing the distortion information of the RD cost of the motion vector prediction (MVP) so as to early terminate the motion search operation. Experimental results show that the proposed fast merge mode estimation achieves a comparable RD performance but reduces the amount of computation by 16.6% on the average when compared to the fast motion estimation with the diamond search implemented in the HM 8.0 reference software.

关键词： video coding visual communications motion estimation mode decision HEVC

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DEN: Disentanglement and Enhancement Networks for Low Illumination images

DEN: Disentanglement and Enhancement Networks for Low Illumi...

引用

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

作者： Bow, Nelson Chong Ngee Vu-Hoang Tran Kerdsiri, Punchok Loh, Yuen Peng Huang, Ching-Chun Multimedia Univ Fac Comp & Informat Cyberjaya Selangor Malaysia Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam PTT Explorat & Prod PCL DCOE Digital Ctr Excellence Bangkok Thailand Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728180687

Though learning-based low-light enhancement methods have achieved significant success, existing methods are still sensitive to noise and unnatural appearance. The problems may come from the lack of structural awareness and the confusion between noise and texture. Thus, we present a low-light image enhancement method that consists of an image disentanglement network and an illumination boosting network. The disentanglement network is first used to decompose the input image into image details and image illumination. The extracted illumination part then goes through a multi-branch enhancement network designed to improve the dynamic range of the image. The multi-branch network extracts multi-level image features and enhances them via numerous subnets. These enhanced features are then fused to generate the enhanced illumination part. Finally, the denoised image details and the enhanced illumination are entangled to produce the normal-light image. Experimental results show that our method can produce visually pleasing images in many public datasets

关键词： low-light enhancement image disentanglement multi-branch enhancement network

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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

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

作者： Gao, Shuo Zhang, Yueyi Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728180687

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Volumetric image compression 3D convolutional neural network convolutional long short-term memory

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Video Denoising Algorithm via Multi-scale Joint Luma-Chroma Bilateral Filter

Video Denoising Algorithm via Multi-scale Joint Luma-Chroma ...

引用

ieee visual communications and image processing (vcip) conference

作者： Gao, Yuanyuan Hu, Hai-Miao Wu, Jiawei Beihang Univ Beijing Key Lab Digital Media Sch Comp Sci & Engn Beijing 1100191 Peoples R China

ISBN: (纸本)9781467373142

Video denoising is important for display and subsequent analysis, but remains to be a challenging problem. Key insights that limit the performance of algorithms include two main aspects. First, low-frequency scene information and the coarse-grained noise in the chroma are mixed with each other, which is different from that in the luma. Thus, denoising the chroma by using only its own information is difficult. Second, it is impossible to directly use the denoised luma edge information to guide the chroma denoising for high quality results. Therefore, in this paper, we propose a multi-scale joint luma-chroma bilateral filter to improve chroma denoising performance. Experimental results demonstrate that the proposed algorithm surpasses state-of-the-art algorithms in most cases.

关键词： Video Denoising Chroma denoising Bilateral Filter image enhancement Structured noise

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SALIENT OBJECT DETECTION IN image SEQUENCES VIA SPATIAL-TEMPORAL CUE

SALIENT OBJECT DETECTION IN IMAGE SEQUENCES VIA SPATIAL-TEMP...

引用

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

作者： Gan, Chuang Qin, Zengchang Xu, Jia Wan, Tao Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100084 Peoples R China Beihang Univ Intelligent Comp & Machine Learning Lab Beijing Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing Peoples R China

ISBN: (纸本)9781479902880

Contemporary video search and categorization are non-trivial tasks due to the massively increasing amount and content variety of videos. We put forward the study of visual saliency models in video. Such a model is employed to identify salient objects from the image background. Starting from the observation that motion information in video often attracts more human attention compared to static images, we devise a region contrast based saliency detection model using spatial-temporal cues (RCST). We introduce and study four saliency principles to realize the RCST. This generalizes the previous static image for saliency computational model to video. We conduct experiments on a publicly available video segmentation database where our method significantly outperforms seven state-of-the-art methods with respect to PR curve, ROC curve and visual comparison.

关键词： object detection saliency spatial-temporal cue

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KonIQ-10k-LT: Overcoming Score Priors in Blind image Quality Assessment Under Imbalanced Distributions

KonIQ-10k-LT: Overcoming Score Priors in Blind Image Quality...

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2024 conference on visual communications and image processing

作者： Yuan, Desen Wang, Lei Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Blind image Quality Assessment (BIQA) is essential in computational vision for predicting the visual quality of digital images without reference counterparts. Despite advancements through convolutional neural networks (CNNs), a significant challenge in BIQA remains the long-tail distribution of image quality scores, leading to biased training and reduced model generalization. To address this, we restructured the KonIQ-10k dataset to create an imbalanced version named KonIQ-10k-LT, manipulating the distribution of image quality scores to have opposing distributions in the training and validation sets. This restructuring increases the proportion of certain quality scores in the training set while decreasing them in the validation set. Experimental results show a significant performance decline of BIQA models on the KonIQ-10k-LT dataset compared to the original KonIQ-10k, highlighting the challenge posed by the long-tail distribution. To mitigate this issue, we propose a Proportion Weighted Balancing (PWB) method as a baseline, designed to enhance the robustness and generalization ability of BIQA models. Our findings demonstrate that the proposed WB method improves the performance and reliability of BIQA models under these challenging conditions.

关键词： Blind image quality assessment Long-tail learning

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