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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151-160 订阅

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

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

作者： Zhu, Xun Xu, Mengqiu Wu, Ming Zhang, Chuang Zhang, Bin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665475921

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of international Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18%, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69%, respectively.

关键词： sea fog recognition weakly supervised learning point annotation semantic segmentation remote sensing image

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Spatio-spectral image Reconstruction Using Non-local Filtering

Spatio-spectral Image Reconstruction Using Non-local Filteri...

引用

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

作者： Sippel, Frank Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781728185514

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.

关键词： Spatio-spectral Reconstruction Error Concealment Linear Regression Block Matching Non-Local Filtering

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Mask-Guided Transformer for Human-Object Interaction Detection

Mask-Guided Transformer for Human-Object Interaction Detecti...

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

作者： Ying, Daocheng Yang, Hua Sun, Jun Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665475921

Human-object interaction (HOI) detection is a meaningful research topic on human activity understanding. Recent works have made significant progress by focusing on efficient triplet matching and leveraging image-wide features based on encoder-decoder architecture. However, the ability to gather relevant contextual information about human is limited and different sub-tasks in HOI detection are not differentiated by specific decoupling in previous methods. To this end, we propose a new transformer-based method for HOI detection, namely, Mask-Guided Transformer (MGT). Our model, which is composed of five parallel decoders with a shared encoder, not only emphasizes interactive regions by applying body features, but also disentangles the prediction of instance and interaction. We achieve a favorable result at 63.3 mAP on the well-known HOI detection dataset V-COCO.

关键词： Transformers Object detection Human and object interaction HOI detection

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CRC-Based Multi-Error Correction of H.265 Encoded Videos in Wireless communications

CRC-Based Multi-Error Correction of H.265 Encoded Videos in ...

引用

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

作者： Boussard, Vivien Coulombe, Stephane Coudoux, Francois-Xavier Corlay, Patrick Trioux, Anthony Ecole Technol Super Dept Software & IT Engn Montreal PQ Canada Univ Lille CNRS UPHF ISENCent LilleUMR 8520IEMNDOAE Valenciennes France

ISBN: (纸本)9781728185514

This paper analyzes the benefits of extending CRC-based error correction (CRC-EC) to handle more errors in the context of error-prone wireless networks. In the literature, CRC-EC has been used to correct up to 3 binary errors per packet. We first present a theoretical analysis of the CRC-EC candidate list while increasing the number of errors considered. We then analyze the candidate list reduction resulting from subsequent checksum validation and video decoding steps. Simulations conducted on two wireless networks show that the network considered has a huge impact on CRC-EC performance. Over a Bluetooth low energy (BLE) channel with Eb/No=8 dB, an average PSNR improvement of 4.4 dB on videos is achieved when CRC-EC corrects up to 5, rather than 3 errors per packet.

关键词： Cyclic Redundancy Check Error Correction H.264 H.265 Wireless communications

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UNIVERSAL AND LOW-COMPLEXITY QUANTIZER DESIGN FOR COMPRESSIVE SENSING image CODING

UNIVERSAL AND LOW-COMPLEXITY QUANTIZER DESIGN FOR COMPRESSIV...

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

作者： Li, Xiangwei Lan, Xuguang Yang, Meng Xue, Jianru Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

ISBN: (纸本)9781479902880

Compressive sensing imaging (CSI) is a new framework for image coding, which enables acquiring and compressing a scene simultaneously. The CS encoder shifts the bulk of the system complexity to the decoder efficiently. Ideally, implementation of CSI provides lossless compression in image coding. In this paper, we consider the lossy compression of the CS measurements in CSI system. We design a universal quantizer for the CS measurements of any input image. The proposed method firstly establishes a universal probability model for the CS measurements in advance, without knowing any information of the input image. Then a fast quantizer is designed based on this established model. Simulation result demonstrates that the proposed method has nearly optimal rate-distortion (R similar to D) performance, meanwhile, maintains a very low computational complexity at the CS encoder.

关键词： Compressive sensing imaging quantization Gaussian distribution image coding

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A Secure Network Coding based image communications in Two-Hop Wireless Relay Networks

A Secure Network Coding based Image Communications in Two-Ho...

引用

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

作者： Vien, Quoc-Tuan Nguyen, Tuan T. Middlesex University Faculty of Science and Technology United Kingdom University of Greenwich School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350359855

This paper investigates the image communications in two-hop wireless relay networks (TH-WRNs) where a source node sends images to a destination node with the assistance of a relay node via two hops, i.e. source-To-relay and relay-To-destination links. Due to the broadcast nature of wireless media, there exists an eavesdropper who tries to overhear and recover the images. Aiming to enhance the security and also to save transmission bandwidth of the image communications, we propose a secure relaying transmission (SRT) protocol by exploiting both random linear network coding (RLNC) and image super-resolution (ISR) techniques. In the proposed protocol, the original high-resolution (HR) images are downscaled at the source node and the RLNC is employed at both the source and relay nodes to conceal the original images from the eavesdropper. The RLNC decoding and ISR are adopted at the destination node to decode and recover the HR images, while the eavesdropper cannot decode the images due to the unawareness of the coefficient matrices and the reference images in the RLNC. It is shown that the proposed SRT protocol achieves a significantly higher performance at the destination than at the eavesdropper. Furthermore, with high-quality relaying hops, the SRT protocol outperforms the secure direct transmission (SDT) protocol with only a direct link between the source and the destination nodes. Finally, simulation results are provided to verify the findings. © 2023 ieee.

关键词： Decoding

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DIRECT: Discrete image Rescaling with Enhancement from Case-specific Textures

DIRECT: Discrete Image Rescaling with Enhancement from Case-...

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

作者： Yan-An Chen Ching-Chun Hsiao Wen-Hsiao Peng Ching-Chun Huang Natl Yang Ming Chiao Tung Univ Inst Comp Sci & Engn Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728185514

This paper addresses image resealing, the task of which is to downscale an input image followed by upscaling for the purposes of transmission, storage, or playback on heterogeneous devices. The state-of-the-art image resealing network (known as IRN) tackles image downscaling and upscaling as mutually invertible tasks using invertible affine coupling layers. In particular, for upscaling, IRN models the missing high-frequency component by an input-independent (case-agnostic) Gaussian noise. In this work, we take one step further to predict a case-specific high-frequency component from textures embedded in the downscaled image. Moreover, we adopt integer coupling layers to avoid quantizing the downscaled image. When tested on commonly used datasets, the proposed method, termed DIRECT, improves high-resolution reconstruction quality both subjectively and objectively, while maintaining visually pleasing downscaled images.

关键词： Single image Resealing Discrete Invertible Neural Network Case-specific network

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Fine-grained Single-layer Tiling for Viewport-Adaptive 360-degree Video Streaming

Fine-grained Single-layer Tiling for Viewport-Adaptive 360-d...

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

作者： Jeong, Jong-Beom Park, Jun-Hyeong Lee, Soonbin Ryu, Eun-Seok Department of Computer Science Education Seoul Korea Republic of Department of Immersive Media Engineering Seoul Korea Republic of Multimedia Communications Group Berlin Germany

ISBN: (纸本)9798350359855

Tile-based streaming is widely adopted for viewport-Adaptive 360-degree video streaming due to its potential for bitrate reduction. However, for legacy devices equipped with a single decoder, managing multiple tile bitstreams poses a challenge, particularly with regards to synchronizing multiple decoders. This paper introduces Butterfly360, a fine-grained single-layer tiling method that advances two main ideas: (i) an optimal tiling scheme decision method for single-layer merging of tiles using various tile sizes, and (ii) a simple rate adaptation method for viewport tiles. Experimental results demonstrate that the proposed method offers advantages in terms of bitrate savings and reduction in decoding runtime. Furthermore, the proposed method facilitates the use of either multiple tile bitstreams or a single-layer merged bitstream. © 2023 ieee.

关键词： Video streaming

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Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based image Coding

Large-Scale Crowdsourcing Subjective Quality Evaluation of L...

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

作者： Upenik, Evgeniy Testolina, Michela Ascenso, Joao Pereira, Fernando Ebrahimi, Touradj Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp Lausanne Switzerland Inst Super Tecn Inst TelecomunicacOes Lisbon Portugal

ISBN: (纸本)9781728185514

Learning-based image codecs produce different compression artifacts, when compared to the blocking and blurring degradation introduced by conventional image codecs, such as JPEG, JPEG 2000 and HEIC. In this paper, a crowdsourcing based subjective quality evaluation procedure was used to benchmark a representative set of end-to-end deep learning-based image codecs submitted to the MMSP'2020 Grand Challenge on Learning-Based image Coding and the JPEG AI Call for Evidence. For the first time, a double stimulus methodology with a continuous quality scale was applied to evaluate this type of image codecs. The subjective experiment is one of the largest ever reported including more than 240 pair-comparisons evaluated by 118 naive subjects. The results of the benchmarking of learning-based image coding solutions against conventional codecs are organized in a dataset of differential mean opinion scores along with the stimuli and made publicly available.

关键词： deep learning image coding learning-based compression subjective evaluation visual quality crowdsourcing

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AutoDerain: Memory-efficient Neural Architecture Search for image Deraining

AutoDerain: Memory-efficient Neural Architecture Search for ...

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

作者： Fu, Jun Hou, Chen Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

ISBN: (纸本)9781728185514

Learning-based image deraining methods have achieved remarkable success in the past few decades. Currently, most deraining architectures are developed by human experts, which is a laborious and error-prone process. In this paper, we present a study on employing neural architecture search (NAS) to automatically design deraining architectures, dubbed AutoDerain. Specifically, we first propose an U-shaped deraining architecture, which mainly consists of residual squeeze-andexcitation blocks (RSEBs). Then, we define a search space, where we search for the convolutional types and the use of the squeeze-and-excitation block. Considering that the differentiable architecture search is memory-intensive, we propose a memory-efficient differentiable architecture search scheme (MDARTS). In light of the success of training binary neural networks, MDARTS optimizes architecture parameters through the proximal gradient, which only consumes the same GPU memory as training a single deraining model. Experimental results demonstrate that the architecture designed by MDARTS is superior to manually designed derainers.

关键词： memory-efficient differentiable architecture search image deraining

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