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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 ...

引用

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...

引用

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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DDIN: Enhancing Food Ingredient Recognition with Region and Category Discovery Modules

DDIN: Enhancing Food Ingredient Recognition with Region and ...

引用

IEEE visual communications and image processing (VCIP)

作者： Yiheng Ru Huaiyan Jiang Hang Song Bo Wei Yu Liu School of Microelectronics Tianjin University China School of Environment and Society Tokyo Institute of Technology Tokyo Japan School of Engineering The University of Tokyo Saitama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Food ingredient recognition has received numerous attention for its importance for health-related applications. But there are still some challenges due to food dishes complexity such as detecting ingredients of varying sizes and identifying multiple ingredients within a single image. Addressing this challenge, we propose a novel ingredient recognition network named Dual Discovery Integration Network (DDIN) which consists of two modules: the Region Discovery (RD) module using deconvolution to get probability distribution map for find-grained region discover, the Category Discovery (CD) module using an ingredient dictionary to capture multiple ingredient category. Finally, the output from RD and CD modules are fused to obtain the final prediction results. The experimental results demonstrate that our model achieves state-of-the-art results in ingredient recognition on the Chinese Food dataset Vireo Food-172, and it also outperform existing methods for less parameters and lower computational complexity. Further visualization of discovered ingredient regions also shows the superiority of our method.

关键词： visualization image recognition Accuracy Graphical models Dictionaries Deconvolution visual communication Computational modeling Probability distribution Distribution functions

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image Captioning Algorithm Based on Sufficient visual Information and Text Information 27th

Image Captioning Algorithm Based on Sufficient Visual Inform...

引用

27th International conference on Neural Information processing

作者： Zhao, Yongqiang Rao, Yuan Wu, Lianwei Feng, Cong Xi An Jiao Tong Univ Xian 710049 Peoples R China

ISBN: (纸本)9783030638221;9783030638238

Most existing attention-based methods on image captioning focus on the current visual information and text information at each step to generate the next word, without considering the coherence between the visual information and the text information itself. We propose sufficient visual information (SVI) module to supplement the existing visual information contained in the network, and propose sufficient text information (STI) module to predict more text Words to supplement the text information contained in the network. Sufficient visual information module embed the attention value from the past two steps into the current attention to adapt to human visual coherence. Sufficient text information module can predict the next three words in one step, and jointly use their probabilities for inference. Finally, this paper combines these two modules to form an image captioning algorithm based on sufficient visual information and text information model (SVITI) to further integrate existing visual information and future text information in the network, thereby improving the image captioning performance of the model. These three methods are used in the classic image captioning algorithm, and have achieved achieve significant performance improvement compared to the latest method on the MS COCO dataset.

关键词： image captioning Sufficient visual information Sufficient text information

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Hybrid Implicit Neural image Compression with Subpixel Context Model and Iterative Pruner

Hybrid Implicit Neural Image Compression with Subpixel Conte...

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IEEE visual communications and image processing (VCIP)

作者： Hangyu Li Wei Jiang Litian Li Yongqi Zhai Ronggang Wang Shenzhen Graduate School Peking University Shenzhen China Pengcheng Laboratory Shenzhen China

Recently, image compression based on Implicit Neural Representation (INR) has gained interest. However, the performance of INR-based methods is poor because MLP can not fit high-frequency information well. Recently, a kind of hybrid INR method was proposed, which transmits not only the parameters of MLP but also some external latents to help to overfit at the decoder. Despite its low MACs (Multiply–Accumulate Operations per pixel), the decoding time is still slow due to the serial autoregressive context model for entropy coding. Hence, we propose two methods to improve the hybrid INR method’s decoding speed and performance. First, the Subpixel Context Model (SAM) is aimed to speed up the process of entropy coding. The proposed SAM is latent by latent instead of pixel by pixel. Second, we add an iterative pruner to the pipeline of hybrid INR compression, to further reduce the bit rate. The performance and MACs of our approach are on par with the SOTA of the hybrid INR methods. The decoding speed is about eight times faster on CPU, and three times faster on GPU.

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Learned Progressive image Compression With Spatial Autoregression

Learned Progressive Image Compression With Spatial Autoregre...

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IEEE visual communications and image processing (VCIP)

作者： Wenxin Tian Shaohui Li Wenrui Dai Cewu Lu Weisheng Hu Lin Zhang Junfeng Du Hongkai Xiong Shanghai Jiao Tong University Tsinghua Shenzhen International Graduate School Shanghai Cogent Technologies Co. Ltd. Caton Technology (Shanghai) Limited

Entropy modeling plays an important role in estimating the rates of latent representations and optimizing the rate-distortion performance for learned image compression. Autoregression modules are demonstrated to eliminate spatial/channel-wise redundancy of latent representations in fixed-rate learned image compression. However, it cannot be efficiently achieved in progressive coding due to the high computational complexity raised by element-wise probability prediction. In this paper, we propose a learned progressive image compression method that enables spatial autoregression for entropy modeling. Specifically, we develop a novel codeword alignment scheme to prevent coding redundancy and achieve efficient autoregression of latent representations in different quality layers. Consequently, conditional probability estimation for the latent prediction can be achieved based on spatial autoregression in a layer-wise manner. We further extend the proposed method by dead-zone quantizers to obtain promoted rate-distortion performance. The proposed method is a successful attempt to enable spatial autoregression in learned progressive coding and further bridge the performance gap with fixed-rate models. Experimental results show that it outperforms traditional methods such as JPEG and BPG, as well as recent fine-grained learned progressive coding models DPICT and PLONQ in terms of rate-distortion performance.

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