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DISTINGUISHING COMPUTER-GENERATED imageS FROM PHOTOGRAPHIC imageS: A TEXTURE-AWARE DEEP LEARNING-BASED METHOD

DISTINGUISHING COMPUTER-GENERATED IMAGES FROM PHOTOGRAPHIC I...

引用

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

作者： Zhang, Zicheng Sun, Wei Min, Xiongkuo Wang, Tao Lu, Wei Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665475921

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.

关键词： Computer-generated photographic image classification dual-stream neural network texture map

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Segmentation-based Initialization for Steered Mixture of Experts

Segmentation-based Initialization for Steered Mixture of Exp...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Li, Yi-Hsin Sjostrom, Marten Knorr, Sebastian Sikora, Thomas Technical University of Berlin Germany Mid Sweden University Sweden Ernst-Abbe University of Applied Sciences Jena Germany

ISBN: (纸本)9798350359855

The Steered-Mixture-of-Experts (SMoE) model is an edge-Aware kernel representation that has successfully been explored for the compression of images, video, and higher-dimensional data such as light fields. The present work aims to leverage the potential for enhanced compression gains through efficient kernel reduction. We propose a fast segmentation-based strategy to identify a sufficient number of kernels for representing an image and giving initial kernel parametrization. The strategy implies both reduced memory footprint and reduced computational complexity for the subsequent parameter optimization, resulting in an overall faster processing time. Fewer kernels, when combined with the inherent sparsity of the SMoEs, further enhance the overall compression performance. Empirical evaluations demonstrate a gain of 0.3-1.0 dB in PSNR for a constant number of kernels, and the use of 23 % less kernels and 25 % less time for constant PSNR. The results highlight the feasibility and practicality of the approach, positioning it as a valuable solution for various image-related applications, including image compression. © 2023 IEEE.

关键词： image compression

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Multi-Class image Generation from EEG Features with Conditional Generative Adversarial Networks 15

Multi-Class Image Generation from EEG Features with Conditio...

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15th IEEE International conference on Wireless communications and Signal processing, WCSP 2023

作者： Duan, Yiping Hu, Shuzhan Ma, Xin Tao, Xiaoming Tsinghua University Department of Electronic Engineering Beijing China Beijing China

ISBN: (纸本)9798350324662

Exploring the visual coding mechanism of the human brain has been an important topic in brain-computer interface technology, holding the potential to provide new insights into the research of the Internet of Things (IoT). Especially, the use of brain signals for image generation has attracted people's attention. In this paper, we propose a multi-class image generation method based on electroencephalography (EEG) features with conditional generative adversarial networks (CGAN). Specifically, we synchronously collect the EEG signals when subjects watch visual stimuli through EEG equipment and construct a multi-category image-EEG dataset. The obtained EEG dataset undergoes preprocessing to eliminate interference from electrooculographic signals and electromyographic signals. Based on this dataset, we extract EEG features related to visual stimuli by deep learning algorithms and integrate them with CGAN for image generation. We employ different EEG feature extraction methods for image generation and conduct comparative experiments. The experimental results demonstrate that the proposed method based on the CGAN model utilizing the joint EEG spatio-temporal and temporal-frequency features can achieve better performance in the image generation task. © 2023 IEEE.

关键词： CGAN Deep Learning EEG image Generation Joint Features

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Lossless Compression of Raw images by Learning the Prediction and Frequency Decomposition

Lossless Compression of Raw Images by Learning the Predictio...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Rhee, Hochang Cho, Nam Ik Seoul National University Department of ECE INMC Seoul Korea Republic of

ISBN: (纸本)9798350359855

This paper presents a lossless color filter array (CFA) image compression method that attempts to maximize the use of image correlations from two perspectives. First, we adopt a hierarchical approach for decomposing an input into subimages and proceed with the subimage encoding with a new encoding order. The new encoding scheme is shown to provide better prediction performances compared to conventional raster scan orders. Secondly, for each subimage, we compress the low-frequency components first and use them as additional input for encoding the remaining high-frequency components. In this scenario, the high-frequency components show improved compression efficiency since they use the low-frequency ones as strong prior. Experiments show that the proposed method achieves state-of-The-Art performance for 8∼16 bits raw images. © 2023 IEEE.

关键词： image compression

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Adaptive and Collaborative Multi-scale Alignment for Text-Based Person Search

Adaptive and Collaborative Multi-scale Alignment for Text-Ba...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Yang, Xinxin Pan, Renjie Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute China

ISBN: (纸本)9798350359855

Text-To-image person search is challenging due to the cross-scale correspondences and information inequality between modalities. Specifically, images and text are complexly linked at different scales and images are usually more informative and complete than text. It is crucial to establish semantic correlations between modalities and focus on task-relevant information in images. In this paper, we propose a novel Adaptive and Collaborative Multi-scale Alignment network (ACMA) for text-based person search that learns semantically consistent and information-Aligned multi-modal representations. Firstly, we introduce a novel joint embedding module that adaptively integrates features of different pixels and words, thereby extracting semantically consistent multi-modal features at different scales. Second, we design a cross-modal fusion feature-based auxiliary visual branch to guide the extraction of key visual features that are beneficial for cross-modal matching. Extensive experiments validate that ACMA outperforms the state-of-The-Art method. © 2023 IEEE.

关键词： Embeddings

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Game Character Generation with Generative Adversarial Networks 30

Game Character Generation with Generative Adversarial Networ...

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30th IEEE Signal processing and communications Applications conference (SIU)

作者： Emekligil, Ferda Gul Aydin Oksuz, Ilkay Istanbul Tech Univ Oyun & Etkilesim Teknol Bolumu Istanbul Turkey Istanbul Tech Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781665450928

Designing visual content and characters for games is a time consuming task even for designers and illustrators with experience. Most of the game companies and developers use procedural methods to automate the design process. The visual content produced by these algorithms is limited in terms of variation. In this paper, we propose to use Generative Adversarial Networks (GANs) for visual content production. Two different rpg and dnd visual image datasets were collected over the internet for training and 6 different GAN models were trained on them. In 3 of 18 experiments, transfer learning methods are used because of the limited datasets. The Frechet Inception Distance metric was used to compare the model results. As a result, SNGAN was the most successful in both datasets. Moreover, the transfer learning method (WGAN-GP, BigGAN) was more successful than the from scratch method.

关键词： Generative Adversarial Network Generative Learning image Generation Game Character Generation

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Hybrid Light Field image Denoising Network using 4D-DCT Separated Transform

Hybrid Light Field Image Denoising Network using 4D-DCT Sepa...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350359855

This paper proposes a novel hybrid light field (LF) denoising method which is based on a convolutional neural network (CNN) designed to reflect the characteristic of LF image in both pixel and frequency domains. Noting that the image noise usually has much high-frequency energy, the proposed network is designed to operate in a transform domain in two stages. At the first stage, energy compaction of spatial-angular information of LF image is sought by 4D-DCT separated transform which can achieve better energy compaction than 2D-DCT applied separately in the spatial and angular domain. The transformed LF is decomposed into different frequency components and each frequency component is recovered progressively. Subsequently, we reshape and convert different frequency components into pixel domain to perform the next refinement step for which a residual spatial-angular block (RSAB) is proposed to handle the 4D LF structure in the pixel domain. Extensive experimental results on different noisy datasets confirm the effectiveness of our proposed method compared to state-of-the-art methods in both objective and subjective quality. © 2023 IEEE.

关键词： image denoising

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USGG: UNION MESSAGE BASED SCENE GRAPH GENERATION 30

USGG: UNION MESSAGE BASED SCENE GRAPH GENERATION

引用

30th IEEE International conference on image processing (ICIP)

作者： Sun, Shiqi Huang, Danlan Qin, Zhijin Tao, Xiaoming Pan, Chengkang Liu, Guangyi Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China China Mobile Res Inst Xicheng Peoples R China

ISBN: (纸本)9781728198354

Scene graph generation (SGG) is designed to represent images by objects and their relationships. Existing works mainly attempt to strengthen object pair representations for SGG. However, most methods ignore the significant semantic information implied in union regions, which refers to the surrounding area of object pairs. In this paper, we propose a new union message based architecture, named as USGG, to profoundly exploit the relational semantics of unions to facilitate SGG. Concretely, we employ sufficient feature extraction to enhance the features of objects and unions. Next, we devise the Union Embedding Network to model the relational representations through two symmetric encoder-decoder branches. Moreover, the Union Fusion Network is designed to integrate the refined semantics by two-stage feature fusion. Extensive experiments are conducted on visual Genome dataset, which demonstrates that the proposed approach achieves competitive performance against state-of-the-art methods on Recall, mean Recall and Zero Shot Recall metrics.

关键词： Scene Graph Generation visual Relationship Detection Scene Understanding image Representations

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Overfitting NN loop-filters in video coding

Overfitting NN loop-filters in video coding

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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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MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES 30

MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES

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30th IEEE International conference on image processing (ICIP)

作者： Lee, Yegi Kim, Shin Yoon, Kyoungro Lim, Hanshin Kwak, Sangwoon Choo, Hyon-Gon Konkuk Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9781728198354

Conventional video coding methods have been developed based on the human visual system (HVS). However, in recent years, video has occupied a huge portion of internet traffic, and the mount of video data for machine consumption has increased rapidly due to the progress of neural networks. This paper proposes a novel machine-attention-based video coding method for machines. Inspired by the saliency-driven research, we first extract attention regions, sensitively affecting the machine vision performance, from the object detection network. Subsequently, a maximum a posterior (MAP)-based bit allocation method is applied to assign more bits to the attention regions. Our proposed method helps to maintain high machine vision performance whereas reducing the bitrate. Experimental results show that our proposed method achieves up to 34.89% bjontegaard delta (BD)-rate reduction for the video dataset and up to 44.70% BD-rate reduction for the image dataset compared to state-of-the-art video coding technology.

关键词： Video Coding for Machines Video Compression image Compression Machine-Attention MPEG VCM

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