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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171-180 订阅

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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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Dual-stream Self-attention Network for image Captioning

Dual-stream Self-attention Network for Image Captioning

引用

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

作者： Wan, Boyang Jiang, Wenhui Fang, Yuming Wen, Wenying Liu, Hantao Jiangxi Univ Finance & Econ Nanchang Jiangxi Peoples R China Cardiff Univ Cardiff Wales

ISBN: (纸本)9781665475921

Self-attention based encoder-decoder models achieve dominant performance in image captioning. However, most existing image captioning models (ICMs) only focus on modeling the relation between spatial tokens, while channel-wise attention is neglected for getting visual representation. Considering that different channels of visual representation usually denote different visual objects, it may lead to poor performance in terms of object and attribute words in the captioning sentences generated by the ICMs. In this paper, we propose a novel dual-stream self-attention module (DSM) to alleviate the above issue. Specifically, we propose a parallel self-attention based module that simultaneously encodes visual information from the spatial and channel dimensions. Besides, to obtain channel-wise visual features effectively and efficiently, we introduce a group self-attention block with linear computational complexity. To validate the effectiveness of our model, we conduct extensive experiments on the standard IC benchmarks including MSCOCO and Flickr30k. Without bells and whistles, the proposed model performs new SOTAs containing 135.4 CIDEr score on MSCOCO and 70.8 CIDEr score on Flickr30k.

关键词： image Captioning Self-Attention Spatial Attention Channel Attention

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Learning in Compressed Domain for Faster Machine Vision Tasks

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

引用

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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Entropy of Primitive: A Top-Down Methodology for Evaluating the Perceptual visual Information

Entropy of Primitive: A Top-Down Methodology for Evaluating ...

引用

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

作者： Zhang, Xiang Wang, Shiqi Ma, Siwei Liu, Shaohui Gao, Wen Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Peking Univ Inst Digital Media Beijing 100871 Peoples R China

ISBN: (纸本)9781479902880

In this paper, we aim at evaluating the perceptual visual information based on a novel top-down methodology: entropy of primitive (EoP). The EoP is determined by the distribution of the atoms in describing an image, and is demonstrated to exhibit closely correlation with the perceptual image quality. Based on the visual information evaluation, we further demonstrate that the EoP is effective in predicting the perceptual lossless of natural images. Inspired by this observation, in order to distinguish whether the loss of input signal is visual noticeable to human visual system (HVS), we introduce the EoP based perceptual lossless profile (PLP). Extensive experiments verify that, the proposed EoP based perceptual lossless profile can efficiently measure the minimum noticeable visual information distortion and achieve better performance compared to the-state-of-the-art just-noticeable difference (JND) profile.

关键词： Entropy of primitive visual information just-noticeable difference perceptual lossless profile

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AN IMPROVED image COMPRESSION SCHEME WITH AN ADAPTIVE PARAMETERS SET IN ENCRYPTED DOMAIN

AN IMPROVED IMAGE COMPRESSION SCHEME WITH AN ADAPTIVE PARAME...

引用

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

作者： Zhang, Guochao Liu, Shaohui Jiang, Feng Zhao, Debin Gao, Wen Harbin Inst Technol Dept Comp Sci & Technol Harbin 150006 Peoples R China

ISBN: (纸本)9781479902880

A growing societal awareness about privacy and security push the development of signal processing techniques in the encrypted domain. Data compression in encrypted domain attracts much attention recently years due to its avoiding the leakage of data source during compression. This paper proposes an improved block-by-block compression scheme of encrypted image with flexible compression ratio. The original image is encrypted by permuting the blocks of the image and then permuting the pixels in the blocks. In the compression, pixels chosen randomly used as reference information, and remaining pixels are compressed by coset code. At the decoder side, side information (SI) which is generated by combining correlation among blocks and image restoration from partial random samples (IRPRS) is utilized to assist the decompression. Moreover, an adaptive system parameters selection method is also given in this paper. The experimental results show that the proposed method can achieve a better reconstructed result compared with the earlier method.

关键词： image compression image encryption coset code image reconstruction

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A Practical Approach to Depth-Aware Augmentation for Neural Radiance Fields

A Practical Approach to Depth-Aware Augmentation for Neural ...

引用

2024 conference on visual communications and image processing

作者： Khosroshahi, Hamed Razavi Sancho, Jaime Bonatto, Daniele Fachada, Sarah Bang, Gun Lafruit, Gauthier Juarez, Eduardo Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium Univ Politecn Madrid Res Ctr Software Technol & Multimedia Syst Madrid Spain Telecommun Res Inst Elect Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model's ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

关键词： Neural Radiance Fields NeRF View synthesis Data augmentation Depth map

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Overfitting NN loop-filters in video coding

Overfitting NN loop-filters in video coding

引用

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

作者： Yang, Ruiying Santamaria, Maria Cricri, Francesco Zhang, Honglei Lainema, Jani Youvalari, Ramin G. Hannuksela, Miska M. Elomaa, Tapio Nokia Technologies Tampere Finland Tampere University Tampere Finland

ISBN: (纸本)9798350359855

Overfitting is usually regarded as a negative condition since it impairs the generalisation power of a model. Nevertheless, overfitting a Neural Network (NN) on test data may be advantageous to improve the compression efficiency of image/video coding tools and systems. Previous research has demonstrated the benefits of NN overfitting for post-processing operations, i.e. post-filters, but not yet for actual decoding tools. Generally, the NN is overfitted on test data at the encoder end, and the weight update is coded and sent to the decoder end along the image/video bitstream. The proposed approach follows this strategy. In particular, the overfitting of the Low Operation Point (LOP) loop-filter in NN-based Video Coding (NNVC) software is studied. The overall approach yields Bjontegaard Delta rate (BD-rate) of -7.74%, -13.73% and -12.49%, for the Y, U and V components, respectively. Out of these coding gains, 1.21%, 6.43% and 5.52%, for the Y, U and V components, are attributed to the overfitting. The boost in the coding gains comes with only 1.5% more complexity, due to the multiplier parameters introduced during the overfitting. © 2023 ieee.

关键词： image enhancement

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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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On Data Annotation Efficiency for image Based Crowd Counting

On Data Annotation Efficiency for Image Based Crowd Counting

引用

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

作者： Ma, Tianfang Liu, Shuoyan Wang, Qian Tianjin Univ Tianjin Peoples R China Natl Univ Singapore Singapore Singapore Univ Durham Durham England

ISBN: (纸本)9781665475921

Crowd counting aims at automatically estimating the number of persons in still images. It has attracted much attention due to its potential usage in surveillance, intelligent transportation and many other scenarios. In the recent decade, most researchers have been focusing on the design of novel deep learning models for improved crowd counting performance. Such attempts include proposing advanced architectures of deep neural networks, using different training strategies and loss functions. Other than the capabilities of models, the crowd counting performance is also determined by the quantity and the quality of training data. Whilst the deep models are data-hungry and better performance can usually be expected with more training data, annotating images for training is time-consuming and expensive in real-world applications. In this work, we focus on the efficiency of data annotation for crowd counting. By varying the number of annotated images and the number of annotated points (one point is annotated per person head) for training, our experimental results demonstrate it is more efficient to annotate a small number of points per image across a large number of images for training. Based on this conclusion, we present a novel adaptive scaling mechanism for data augmentation to diversify the training images without extra annotation cost. The mechanism is proved effective via thorough experiments.

关键词： Crowd counting Data annotation efficiency Data augmentation Adaptive scaling

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Single image Super-Resolution Using ConvNeXt

Single Image Super-Resolution Using ConvNeXt

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

作者： You, Chenghui Hong, Chaoqun Liu, Lijuan Lin, Xuehan Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781665475921

In recent years, a lot of deep convolution neural networks have been successfully applied in single image super-resolution (SISR). Even in the case of using small convolution kernel, those methods still require large number of parameters and computation. To tackle the problem above, we propose a novel framework to extract features more efficiently. Inspired by the idea of deep separable convolution, we improve the standard residual block and propose the inverted bottleneck block (IBNB). The IBNB replaces the small-sized convolution kernel with the large-sized convolution kernel without introducing additional computation. The proposed IBNB proves that large kernel size convolution is available for SISR. Comprehensive experiments demonstrate that our method surpasses most methods by up to 0.10 similar to 0.32dB in quantitative metrics with fewer parameters.

关键词： single image super-resolution convolutional neural network deep separable convolution

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