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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201-210 订阅

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ROTATIONAL BASED REWRITABLE DATA HIDING IN JPEG

ROTATIONAL BASED REWRITABLE DATA HIDING IN JPEG

引用

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

作者： Ong, SimYing Wong, KokSheik Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781479902880

This paper proposes a novel rotational method on AC coefficient pairs to embed data into a JPEG compressed image. The purpose is to improve the carrier capacity of [1] while maintaing its original feature in controlling quality degradation. The proposed method exploits two properties in the quantized AC coefficients, namely, large magnitude and short run of zeros for low frequency subbands, and vice versa. The coefficients are first grouped into pairs and then rotated to the left or right directions to create distinctive states, where each can be utilized to represent external data. The AC coefficients are not modified and no additional AC coefficients are introduced for data embedding. However, preprocessing is needed so that all blocks satisfy the properties assumed to ensure correct data extraction and image recovery. The proposed method is rewritable because the host image can be re-utilized without causing further distortion. Experiments were conducted to verify the basic performance of the proposed method. On average, the proposed method is able to embed up to similar to 9318 bits in the test images of quality factor 80.

关键词： Zero run value rotation rewritable data hiding JPEG compressed image DCT

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SHAKING VIDEO SYNTHESIS FOR VIDEO STABILIZATION PERFORMANCE ASSESSMENT

SHAKING VIDEO SYNTHESIS FOR VIDEO STABILIZATION PERFORMANCE ...

引用

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

作者： Qu, Hui Song, Li Xue, Gengjian Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200030 Peoples R China

ISBN: (纸本)9781479902880

The goal of video stabilization is to remove the unwanted camera motion and obtain stable versions. Theoretically, a good stabilization algorithm should remove the unwanted motion without the loss of image qualities. However, due to the lack of ground-truth video frames, the accurate performance evaluation of different algorithms is hard. Most existing evaluation techniques usually synthesize stable videos from shaking ones, but they are not effective enough. Different from previous methods, in this paper we propose a novel method which synthesize shaking videos from stable frames. Based on the synthetic shaking videos, we perform preliminary video stabilization performance assessment on three stabilization algorithms. Our shaking video synthesis method can not only give a benchmark for full-reference video stabilization performance assessment, but also provide a basis for exploring the theoretical bound of video stabilization which may help to improve existing stabilization algorithms.

关键词： Video stabilization ground-truth performance assessment

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Flexible Coding Order for Learned image Compression

Flexible Coding Order for Learned Image Compression

引用

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

作者： Li, Yuqi Zhang, Haotian Liu, Dong University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9798350359855

Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however, may be neither optimal nor efficient. We conduct an elaborate study on coding orders in LIC entropy models, shedding light on the potential for improving compression performance by adapting the coding order. We present Mask Modeling Context Model (MMCM), a transformer-based context model designed with a Patch-restricted Iterative Mask Modeling (PIMM) training strategy. Through training to predict the probability distribution of randomly masked tokens, we are able to use a patch-level arbitrary coding order to encode/decode the latent space in a few iterative steps. Additionally, we employ offline and online rate-distortion optimization to adaptively select the appropriate coding order for each image. Our extensive experimental results demonstrate that the proposed method achieves a better rate-distortion performance than the autoregressive context model while requiring less compression complexity. © 2023 ieee.

关键词： image compression

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WAVELET BASED SMOKE DETECTION METHOD WITH RGB CONTRAST-image AND SHAPE CONSTRAIN

WAVELET BASED SMOKE DETECTION METHOD WITH RGB CONTRAST-IMAGE...

引用

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

作者： Chen, Jiaqiu Wang, Yaowei Tian, Yonghong Huang, Tiejun Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Peking Univ Inst Digital Media Beijing 100871 Peoples R China

ISBN: (纸本)9781479902880

Smoke detection in video surveillance is very important for early fire detection. A general viewpoint assumes that smoke is a low frequency signal which may smoothen the background. However, some pure-color objects also have this characteristic, and smoke also produces high frequency signal because the rich edge information of its contour. In order to solve these problems, an improved smoke detection method with RGB Contrast-image and shape constrain is proposed. In this method, wavelet transformation is implemented based on the RGB Contrast-image to distinguish smoke from other low frequency signals, and the existence of smoke is determined by analyzing the combination of the shape and the energy change of the region. Experimental results show our method outperforms the conventional methods remarkably.

关键词： Smoke detection wavelet transform RGB Contrast-image shape constrain

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Filling the Gap: Enhancing Ultra-Low Light image Brightness Through Multi-Band NIR Estimation

Filling the Gap: Enhancing Ultra-Low Light Image Brightness ...

引用

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

作者： Park, Jeong-Hyeok Park, Tae-Sung Kim, Jong-Ok Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

We propose a method for capturing high-quality images in low-light environments using multi-band near-infrared (NIR) images, which offer robustness to brightness variations and provide structural information not present in RGB images. As multi-band NIR images are not commonly available in consumer cameras, an RGB-NIR conversion network is employed to estimate them. Training data comes from a new low-light dataset with NIR-RGB image pairs. Unlike single-band approaches, this method utilizes multi-band NIR images with peak wavelengths of 785nm, 850nm, and 940nm, resulting in enhanced quality. Integration of the RGB-NIR conversion network into an existing low-light enhancement (LLIE) network is achieved using the proposed cross-attention transformer. Experimental results validate the effectiveness of multi-band NIR estimation in enhancing low-light intensity. Moreover, the proposed RGB-NIR conversion network and cross-attention module can be applied to any existing deep LLIE networks. © 2023 ieee.

关键词： image enhancement

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Special Cane with visual Odometry for Real-time Indoor Navigation of Blind People

Special Cane with Visual Odometry for Real-time Indoor Navig...

引用

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

作者： Tang, Jingyu Hu, Menghan Li, Guodong Li, Qingli Zhang, Jian Zhou, Xiaofeng Zhai, Guangtao East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Minist Educ Key Lab Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728180687

Indoor navigation is urgently needed by blind people in their everyday lives. In this paper, we design an assistive cane with visual odometry based on actual requirements of the blind to aid them in attaining safe indoor navigation. Compared to the state-of-the-art indoor navigation systems, the proposed device is portable, compact, and adaptable. The main specifications of the system are: the perception range is respectively from 0.10m to 2.10m, and 0.08m to 1.60m for width and length dimensions;the maximum weight is 2.1kg;the detection range is from 0.15m and 3.00m;the cruising ability is about 8h;and the objects whose heights are below 80cm can be detected. The demo video of the proposed navigation system is available at: https://***/10.6084/***.12399572.v1.

关键词： obstacle avoidance indoor navigation visually assistive device

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IoU-CLIP: IoU-Aware Language-image Model Tuning for Open Vocabulary Object Detection

IoU-CLIP: IoU-Aware Language-Image Model Tuning for Open Voc...

引用

2024 conference on visual communications and image processing

作者： He, Mingzhou Wu, Qingbo Ngan, King Ngi Xiao, Yiming Meng, Fanman Qiu, Heqian Li, Hongliang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Open vocabulary object detection (OVD), which detects novel categories through detectors trained on base categories, has achieved remarkable advancement attributable to large-scale vision-language models, such as CLIP. The prior OVD works mainly focused on improving the classification accuracy of proposals, ignoring the ability of localization for novel categories. In this work, we propose IoU-aware language-image model tuning (IoU-CLIP) for open vocabulary object detection. Specifically, we construct a region image dataset with different IoU and adopt IoU values as labels to fine-tune the CLIP model to learn IoU-aware and class-agnostic semantic prompts and visual embeddings. The fine-tuned IoU-CLIP can predict IoU scores for proposals, which interact with classification scores. Meanwhile, IoU-aware and class-agnostic visual embeddings are utilized for box regression to enhance the generalization of the localization capability. We evaluate our method on the COCO and LVIS OVD benchmarks, outperforming the baseline (RegionCLIP) by 5.5% AP(50) and 5.8% AP on novel categories, respectively, achieving state-of-the-art performance.

关键词： object detection open vocabulary IoU-aware class-agnostic semantic prompts

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Learned image Compression with Variable Neural Network Architecture

Learned Image Compression with Variable Neural Network Archi...

引用

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

作者： Zeng, Ziyi Yu, Lu Zhejiang University Hangzhou China

ISBN: (纸本)9798350359855

With the development of neural networks, the coding efficiency of learned image compression methods gradually exceeds that of traditional image codecs that are carefully designed and optimized by experts. However, the deep image compression model with an invariable network structure and fixed network parameters can only cover a limited range of bit rates. Using the same network structure for different bit rate ranges leads to sub-optimal coding efficiency. Meanwhile, the more complex network structure and the increase of network parameters cause higher model complexity, which makes it difficult for network training and practical application. In this paper, a new perspective is proposed to solve the above challenges. The idea of variable network architecture is introduced to the field of learned image compression. We devise two strategies with variable network architecture, which improve the coding performance of the learned image compression model from the coding efficiency and complexity respectively. Compared to the image compression method of fixed network structure, our first approach attains an average BDPSNR of 0.771dB and an average BDBR of-14.876%. The second attains an average BDPSNR of 0.320dB and an average BDBR of-5.471%. These results demonstrate the effectiveness of variable network structure in improving coding efficiency and controlling model complexity. © 2023 ieee.

关键词： Network architecture

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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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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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