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UFI: A Unified Feature Interaction Framework for Multi-Label image Classification

UFI: A Unified Feature Interaction Framework for Multi-Label...

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Li, Zhuohua Zhang, Weibo Yang, Ziang Zhu, Fuqing Han, Jizhong Hu, Songlin Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Multi-label image classification (MLIC) is a more challenging task compared with single-label image classification due to multiple concepts targets, and complex visual relationships should be formulated. Convolutional Neural Network (CNN) and visual Transformer (ViT) have shown superior performance in local and global feature representations, respectively. However, the interactions between local and global features are neglected in current works. To further formulate the critical interactions, this paper designs a Unified Feature Interaction (UFI) framework, aiming to integrate the selected local features with global features based on CNN and ViT, simultaneously. The proposed UFI includes two key modules: Class-Related Feature Selection (CRFS) and Feature Interaction Attention (FIA) modules. Specifically, according to the activation map, CRFS selects target regions by the preliminary calculation of predicted scores. FIA enables the significant local-global feature interaction based on the selected target regions and whole image. We initially attempted to interact with local and global features for multi-label image classification. UFI provides a stable improvement over the baseline and produces a new state-of-the-art result on MS-COCO and VOC2007. © 2022 IEEE.

关键词： image classification

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Adaptive Block-Level Quality Parameter Adjustment Towards Low Video Bit-Rate Fluctuation

Adaptive Block-Level Quality Parameter Adjustment Towards Lo...

引用

IEEE visual communications and image processing (VCIP)

作者： Longtao Feng Qian Yin Huiwen Ren Zhao Wang Siwei Ma Yuwen He National Engineering Research Center of Visual Technology School of Computer Science Peking University China Advanced Institute of Information Technology Peking University China Bytedance Inc. San Diego CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Existing quantization parameter (QP) adjustment methods in video coding often focus solely on coding efficiency and ignore the impact of bit-rate fluctuations on video transmission and bandwidth waste. This is mainly because intra pictures, in a hierarchical coding structure, are allocated smaller QP and thus consume more bits. To address this issue, we propose an adaptive block-level QP adjustment method. Specifically, intra picture importance (IPI) is first introduced to evaluate the adjustability of intra picture QP. For intra pictures whose QP can be adjusted, we further propose block importance (BI) to determine their optimal block-level QP adjustment. Experimental results show that our proposed method reduce the bit-rate fluctuations while basically maintaining the coding performance. Notably, significant improvements can be observed in high-resolution videos, with a reduction of approximately 11% in bit-rate fluctuations.

关键词： Video coding Fluctuations image coding Quantization (signal) visual communication Bandwidth Encoding Motion compensation

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Development of GPU-based High-speed Frame Rate Convertor Software for International TV Program Material Exchange

Development of GPU-based High-speed Frame Rate Convertor Sof...

引用

IEEE visual communications and image processing (VCIP)

作者： Ryoichi Kawada Manabu Iguchi KDDI Research Inc. Saitama Japan KDDI Corporation Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Traditionally, when exchanging TV program materials between countries with different standard frame rates, frame rate conversion (FRC) is performed through a real-time TV standards convertor equipment. Recent years have seen an increasing need to perform FRC by software as handling materials as electronic files, rather than VTR tapes, has become more popular. Then it will be more convenient, especially when handling many materials, if the conversion can be performed in a shorter time than the actual length of the material. To meet this requirement while maintaining picture quality, the authors have developed a prototype GPU software of motion- compensated FRC, the algorithm of which is the one used in the current TV standards convertor equipment, without any simplification. It can convert HDTV material files at 6.8 times faster than real time on AWS g4dn.12xlarge, while maintaining the required picture quality. It is expected to make a significant contribution to improving the work efficiency of broadcasters and other operators.

关键词： TV image coding visual communication Software algorithms Prototypes Converters Software Real-time systems Standards HDTV

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image-Prompt Integration Network with Self-Ranking and Inter-Ranking Loss for AI-Generated image Quality Assessment

Image-Prompt Integration Network with Self-Ranking and Inter...

引用

IEEE visual communications and image processing (VCIP)

作者： Tianwei Zhou Xizhang Yao Songbai Tan Xiaoying Ding Guanghui Yue College of Management Shenzhen University Shenzhen China School of Information Engineering Zhongnan University of Economics and Law Wuhan China School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

AI-generated images (AGIs) are increasingly utilized across diverse domains due to their ability to quickly produce high-quality visuals. However, assessing the quality of AGIs re-mains challenging due to their inherent variability and distinctive distortions. To address these challenges, we propose a novel AGI quality assessment method named SIRQA, which enhances feature representation by integrating visual features with textual prompts, effectively measureing the alignment between the generated images and the described content to improve the precision of quality assessment. Specifically, SIRQA employs self-ranking and inter-ranking mechanisms to refine feature representation. The self-ranking mechanism maintains consistency between feature distances and sampling scales, making sure that features from similar sampling scales are positioned closer together. Addition-ally, inter-ranking mechanism sorts the weighted similarity scores between images and prompts to align with the ranking in the label space. Extensive experiments on the AGIQA3K and PKUI2IQA datasets show that our SIRQA outperforms eight state-of-the-art algorithms in terms of both Spearman’s rank correlation coefficient (SRCC) and Pearson linear correlation coefficient (PLCC).

关键词： image quality Correlation coefficient visualization visual communication image synthesis Benchmark testing Distortion Quality assessment Reliability Distortion measurement

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QHash: An efficient hashing algorithm for low-variance image deduplication 23

QHash: An efficient hashing algorithm for low-variance image...

引用

23rd IEEE International conference on High Performance Computing and communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Xuan Chang, Liqiong Liu, Xue School of Computer Science McGill University MontrealQCH3A0G4 Canada

ISBN: (纸本)9781665494571

Attributed to the widespread use of general artificial intelligence (AI), large-scale datasets have become a critical component for the success of AI-powered applications. While collecting a larger dataset is desirable in general, studies have revealed that many databases include duplicated images. The accumulation of redundant images will result in excessive resource usage and inefficient cloud storage utilization. To get rid of the duplicates, hashing-based methods have been developed for the problem of image deduplication. However, as demonstrated in our experiments, current approaches fail in dataset with small visual difference, such as medical images. To this end, we propose QHash, which achieves effective image deduplication on the low-variance dataset. QHash leverages Vector Quantized Variational AutoEncoder (VQ-VAE) to learn the data distribution in an unsupervised manner. In addition, hash sequences are implemented using integer-based tensor, enabling distance calculation and deduplication being processed in parallel on either CPU or GPU. Extensive experiments show that QHash outperforms other baseline approaches by at least 50%, while is about 23% memory efficient and 18% deduplication time speedup. © 2021 IEEE.

关键词： visualization Tensors Smart cities Memory management Graphics processing units Parallel processing Lesions

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Enhancing Global Tetris Packing in V-PCC Through Dynamic Group Partitioning

Enhancing Global Tetris Packing in V-PCC Through Dynamic Gro...

引用

IEEE visual communications and image processing (VCIP)

作者： Yun-Chang Tsai Jui-Chiu Chiang Department of Electrical Engineering National Chung Cheng University Chia-Yi Taiwan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Video-based point cloud compression (V-PCC) adopts a projection-based methodology that leverages established video compression techniques. To maintain temporal consistency within the packed images, V-PCC facilitates global patch packing solutions that aim to align corresponding patches across frames during the packing process. One such technique is global tetris packing (GTP), particularly efficient in scenarios with low-motion activity within point cloud sequences. However, as the length of the point cloud sequence grows, accumulated motion within the input sequence can lead to increasing inconsistencies between patches across images, degrading the effectiveness of GTP. In this paper, we present a novel approach to enhance GTP. Our method measures the similarity between point cloud frames and dynamically partitions the sequence into variable groups of frames (GOFs). This strategy enhances temporal consistency within each group, thereby improving coding efficiency. Experimental results validate the superiority of our proposed technique over the anchor, delivering average BD-rate savings of 3.62%, 3.24%, and 2.16% for D1-PSNR, D2-PSNR, and Y-PSNR, respectively.

关键词： Point cloud compression Optical fibers image coding image recognition Accuracy visual communication Video compression

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Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization

Color Enhancement for V-PCC Compressed Point Cloud via 2D At...

引用

IEEE visual communications and image processing (VCIP)

作者： Jingwei Bao Yu Liu Zeliang Li Shuyuan Zhu Siu-Kei Au Yeung School of Information and Communication Engineering University of Electronic Science and Technology of China School of Science and Technology Hong Kong Metropolitan University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Video-based point cloud compression (V-PCC) converts the dynamic point cloud data into video sequences using traditional video codecs for efficient encoding. However, this lossy compression scheme introduces artifacts that degrade the color attributes of the data. This paper introduces a framework designed to enhance the color quality in the V-PCC compressed point clouds. We propose the lightweight de-compression Unet (LDC-Unet), a 2D neural network, to optimize the projection maps generated during V-PCC encoding. The optimized 2D maps will then be back-projected to the 3D space to enhance the corresponding point cloud attributes. Additionally, we introduce a transfer learning strategy and develop a customized natural image dataset for the initial training. The model was then fine-tuned using the projection maps of the compressed point clouds. The whole strategy effectively addresses the scarcity of point cloud training data. Our experiments, conducted on the public 8i voxelized full bodies long sequences (8iVSLF) dataset, demonstrate the effectiveness of our proposed method in improving the color quality.

关键词： Point cloud compression Geometry image coding Three-dimensional displays image color analysis visual communication Transfer learning Video sequences image reconstruction Optimization

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MSCFormer: Multi-Scale Circular Transformer for image Deblurring

MSCFormer: Multi-Scale Circular Transformer for Image Deblur...

引用

IEEE visual communications and image processing (VCIP)

作者： Shuai Wang Han Wang Renhe Liu Zhipeng Wu Bo Wei Yu Liu School of Microelectronics Tianjin University Tianjin China School of Engineering The University of Tokyo Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Currently, with the extensive application of digital cameras in dynamic capturing, implications such as camera jitter, out-of-focus, and target motion induce various types and degrees of image blurring. Deep learning (DL) is a powerful technique that offers data-adaptive recovery without prior characterization of deblurring filter kernels. However, end-to-end networks can still be improved to restore regions with severe localized blurring. Therefore, we propose a multi-scale circular transformer (MSC-Former) employing averaged neighborhood attention (AvgNA) to solve this problem. It computes the local attention of each feature pixel by learning the correlation between the center and the surrounding windowed neighborhood, then produces integrated attention with direct averaging. We employ a multi-scale circular strategy (MSCS) to compute attention at different spatial scales to expand the receptive field while maintaining a low parameter count. It uses concentric circular regions with varying radii to define neighborhoods at different scales, which expands the receptive field during attention computation while capturing spatial continuity across larger neighborhoods. Experimental results demonstrate that the proposed method surpasses the recent state-of-the-art deblurring techniques on the benchmark dataset.

关键词： Deep learning Runtime Correlation visual communication Dynamics Benchmark testing Jitter Transformers Digital cameras Kernel

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Efficient Bitrate Ladder Construction for Per-Shot Adaptive Encoding

Efficient Bitrate Ladder Construction for Per-Shot Adaptive ...

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IEEE visual communications and image processing (VCIP)

作者： Yan Zhao ZhengXue Cheng Guo Lu Rong Xie Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

HTTP adaptive streaming (HAS) constructs bitrate ladders to deliver videos with the best possible quality under varying network conditions. Though per-shot content adaptive encoding (CAE) largely improves the compression efficiency by constructing the optimal bitrate ladder for each video shot, it suffers from excessive encoding complexity as all the points in the operating space (typically resolution × bitrate) need to be encoded and compared. To address this issue, this paper proposes an efficient bitrate ladder construction method that encodes only a subset of operating points, then uses curve fitting and inter-curve prediction to estimate other points’ RD performance. The proposed method enables low-complexity ladder construction even for high-dimension operating spaces that incorporate dimensions like encoding presets. Experiments show that this method can achieve RD performance comparable to the original per-shot CAE with only 42% encoding points. Even when minimizing the encoding points to 3.6% of the original CAE, it achieves 15% BD-Rate improvements compared to using the fixed bitrate ladder.

关键词： Adaptation models image coding visual communication Bit rate Streaming media Predictive models HTTP Encoding Complexity theory Videos

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Multi-stage Attention Network with Auxiliary Information Refinement for VVC In-loop Filtering

Multi-stage Attention Network with Auxiliary Information Ref...

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IEEE visual communications and image processing (VCIP)

作者： Xin-Yi Cui Zhidao Zhou Zhikai Liu Li Chen Fan Liang School of Electronics and Information Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recently, learning-based video compression techniques have brought significant performance improvements. However, most existing methods have not fully exploit the auxiliary information from the encoding process. To achieve better performance, we propose a multi-stage attention network with auxiliary information refinement for Versatile Video Coding (VVC) in-loop filtering. The proposed network consists of two branches: the main filter branch extracts reconstruction features, while the auxiliary information refinement branch processes prediction and partition. Specifically, the auxiliary information refinement aims to extract features of auxiliary information better to assist in removing compression artifacts. Lastly, we introduce an Auxiliary Information Attention Module (AAM) to fuse the information flow between the two branches. The proposed model is integrated into the NNVC standard software VTM11.0_NNVC-4.0 and tested under all intra configuration. Experimental results show that our method achieves -7.60%, -19.49%, and -20.50% Bjøntegaard-Delta Bit-Rate (BD-BR) improvements across the Y, U, and V components.

关键词： Video coding image coding Fuses visual communication Information filters Feature extraction Data mining Active appearance model image reconstruction Video recording

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