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检索条件"任意字段=Conference on Visual Communications and Image Processing"

共 3471 条记录，以下是161-170 订阅

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AsymLLIC: Asymmetric Lightweight Learned image Compression

AsymLLIC: Asymmetric Lightweight Learned Image Compression

引用

2024 conference on visual communications and image processing

作者： Wang, Shen Cheng, Zhengxue Feng, Donghui Lu, Guo Song, Li Zhang, Wenjun Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymmetric design, where the decoder requires low complexity to cater to diverse low-end devices, while the encoder can accommodate higher complexity to improve coding performance. In this paper, we propose an asymmetric lightweight learned image compression (AsymLLIC) architecture with a novel training scheme, enabling the gradual substitution of complex decoding modules with simpler ones. Building upon this approach, we conduct a comprehensive comparison of different decoder network structures to strike a better trade-off between complexity and compression performance. Experiment results validate the efficiency of our proposed method, which not only achieves comparable performance to VVC but also offers a lightweight decoder with only 51.47 GMACs computation and 19.65M parameters. Furthermore, this design methodology can be easily applied to any LIC models, enabling the practical deployment of LIC techniques.

关键词： Learned image compression neural network lightweight asymmetric

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Deep Motion Flow Aided Face Video De-identification

Deep Motion Flow Aided Face Video De-identification

引用

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

作者： Wen, Yunqian Liu, Bo Xie, Rong Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728185514

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.

关键词： video de-identification video anonymization privacy protection privacy-utility tradeoff

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Rate Controllable Learned image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Saiping Wang, Luge Mao, Xionghui Yang, Fuzheng Wan, Shuai Xidian Univ Sch Telecommun Engn Xian Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose a rate controllable image compression framework, Rate Controllable Variational Autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the stateof-the-art rate controllable image compression networks.

关键词： Deep image compression rate control variational autoencoder rate-distortion

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visual Attention Based image Zero Watermark Scheme With Ensemble Similarity

Visual Attention Based Image Zero Watermark Scheme With Ense...

引用

International conference on Wireless communications and Signal processing (WCSP)

作者： Shi, Jie Yan, Qin Shi, Huijuan Wang, Yapeng Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479903085

Most existing digital image watermarking schemes tend to have the inherent conflicts between imperceptibility and robustness because watermarks are embedded by parameter modification. image watermark techniques resolve this dilemma by extracting invariant features from the image as "embedded" watermark. In this paper we propose an image zero watermark scheme based on visual attention regions of images. In the proposed scheme visual attention model carefully selects top-N salient areas, where a set of selected Scale Invariant Feature transform (SIFT) descriptors are extracted as a watermark. The distance of each pair of SIFT descriptors from the reference and test images are calculated by Kullback-Leibler (KL) divergence after mapping into the high dimensional space respectively. The final distance of two sets of SIFT descriptor are determined by ensemble similarity. The experimental results indicate that the proposed scheme outperforms image zero watermark scheme based on color and edge histograms (CEH) and is robust to attacks of geometric distortion, contrast/luminance distortion and JPEG compression.

关键词： image watermark visual attention ensemble similarity SIFT authentication

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A Privacy-Preserving and End-to-End-Based Encrypted image Retrieval Scheme

A Privacy-Preserving and End-to-End-Based Encrypted Image Re...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Lu, Zhixun Feng, Qihua Li, Peiya Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Beijing Insitute Technol Sch Comp Sci & Technol Beijing Peoples R China Jinan Univ Coll Cyber Secur Guangzhou Peoples R China

ISBN: (纸本)9781665475921

Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achieve satisfied retrieval performance. In this paper, a new end-to-end encrypted image retrieval scheme is presented. First, images are encrypted by using block rotation, new orthogonal transforms and block permutation during the JPEG compression process. Second, we combine the triplet loss and the cross entropy loss to train a network model, which contains gMLP modules, by end-to-end learning for extracting cipher-images' features. Compared with manual features extraction such as extracting color histogram, the end-to-end mechanism can economize on manpower. Experimental results show that our scheme has good retrieval performance, while can ensure compression friendly and no feature leakage.

关键词： JPEG image encryption image retrieval neural network end-to-end

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Learning in Compressed Domain for Faster Machine Vision Tasks

Learning in Compressed Domain for Faster Machine Vision Task...

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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： Liu, Jinming Sun, Heming Katto, Jiro Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan

ISBN: (纸本)9781728185514

Learned image compression (LIC) has illustrated good ability for reconstruction quality driven tasks (e.g. PSNR, MS-SSIM) and machine vision tasks such as image understanding. However, most LIC frameworks are based on pixel domain, which requires the decoding process. In this paper, we develop a learned compressed domain framework for machine vision tasks. 1) By sending the compressed latent representation directly to the task network, the decoding computation can be eliminated to reduce the complexity. 2) By sorting the latent channels by entropy, only selective channels will be transmitted to the task network, which can reduce the bitrate. As a result, compared with the traditional pixel domain methods, we can reduce about 1/3 multiply-add operations (MACS) and 1/5 inference time while keeping the same accuracy. Moreover, proposed channel selection can contribute to at most 6.8% bitrate saving.

关键词： compressed domain image compression face alignment video coding for machine

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The Enhancement of Underexposed images with Blurred Reflectance

The Enhancement of Underexposed Images with Blurred Reflecta...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhou, Jinchao Wan, Renjie Li, Haoliang Kot, Alex C. Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728180687

The images captured in the low-light conditions always suffer from low visibility. Enhancing the visibility of the low-light image is of broad application to various computer vision tasks. Based on the classical Retinex model, previous methods assume the reflectance components as a well-exposed image. In this paper, we introduce the blurring distortion into the Retinex model to cover more general and challenging scenarios. We further propose a two-stage framework to extract the reflectance images and remove the blurring distortion separately. Specifically, we optimize the whole network by embedding a mechanism robust to the pixel misalignment in the training dataset. The experimental results show that our proposed method achieves promising results.

关键词： Low-light image blur illumination reflectance

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Learn A Compression for Objection Detection - VAE with a Bridge

Learn A Compression for Objection Detection - VAE with a Bri...

引用

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

作者： Mei, Yixin Li, Fan Li, Li Li, Zhu Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (纸本)9781728185514

Recent advances in sensor technology and wide deployment of visual sensors lead to a new application whereas compression of images are not mainly for pixel recovery for human consumption, instead it is for communication to cloud side machine vision tasks like classification, identification, detection and tracking. This opens up new research dimensions for a learning based compression that directly optimizes loss function in vision tasks, and therefore achieves better compression performance vis-a-vis the pixel recovery and then performing vision tasks computing. In this work, we developed a learning based compression scheme that learns a compact feature representation and appropriate bitstreams for the task of visual object detection. Variational Auto-Encoder (VAE) framework is adopted for learning a compact representation, while a bridge network is trained to drive the detection loss function. Simulation results demonstrate that this approach is achieving a new state-of-the-art in task driven compression efficiency, compared with pixel recovery approaches, including both learning based and handcrafted solutions.

关键词： image coding for machine object detection learning-based image compression

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Semantic Attribute Guided image Aesthetics Assessment

Semantic Attribute Guided Image Aesthetics Assessment

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Duan, Jiachen Chen, Pengfei Li, Leida Wu, Jinjian Shi, Guangming Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781665475921

image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.

关键词： image aesthetics assessment semantic attributes aesthetic reasoning graph convolutional network

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Aerial image Classification with Label Splitting and Optimized Triplet Loss Learning

Aerial Image Classification with Label Splitting and Optimiz...

引用

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

作者： Liao, Rijun Li, Zhu Bhattacharyya, Shuvra S. York, George Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA Univ Maryland Dept Elect & Comp Engn College Pk MD USA Univ Maryland UMIACS College Pk MD 20742 USA US Air Force Acad Dept Elect & Comp Engn Colorado Springs CO USA US Air Force Acad UAS Res Ctr Colorado Springs CO 80840 USA

ISBN: (纸本)9781728185514

With the development of airplane platforms, aerial image classification plays an important role in a wide range of remote sensing applications. The number of most of aerial image dataset is very limited compared with other computer vision datasets. Unlike many works that use data augmentation to solve this problem, we adopt a novel strategy, called, label splitting, to deal with limited samples. Specifically, each sample has its original semantic label, we assign a new appearance label via unsupervised clustering for each sample by label splitting. Then an optimized triplet loss learning is applied to distill domain specific knowledge. This is achieved through a binary tree forest partitioning and triplets selection and optimization scheme that controls the triplet quality. Simulation results on NWPU, UCM and AID datasets demonstrate that proposed solution achieves the state-of-the-art performance in the aerial image classification.

关键词： Aerial image Classification Label Splitting Semantic Label Appearance Label Optimized Triplet Loss

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