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Predicting total time to compress a video corpus using online inference systems

Predicting total time to compress a video corpus using onlin...

引用

IEEE visual communications and image processing (VCIP)

作者： Xin Shu Vibhoothi Vibhoothi Anil Kokaram Electronic and Electrical Engineering Sigmedia Group Trinity College Dublin Dublin Ireland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Predicting the computational cost of compressing/transcoding clips in a video corpus is important for resource management of cloud services and VOD (Video On Demand) providers. Currently, customers of cloud video services are unaware of the cost of transcoding their files until the task is completed. Previous work concentrated on predicting perclip compression time, and thus estimating the cost of video compression. In this work, we propose new Machine Learning (ML) systems which predict cost for the entire corpus instead. This is a more appropriate goal since users are not interested in per-clip cost but instead the cost for the whole corpus. In this work, we evaluate our systems with respect to two video codecs (x264, x265) and a novel high-quality video corpus. We find that the accuracy of aggregate time prediction for a video corpus is more than two times better than using per-clip predictions. Furthermore, we present an online inference framework in which we update the ML models as files are processed. A consideration of video compute overhead and appropriate choice of ML predictor for each fraction of corpus completed yields a prediction error of less than 5%. This is approximately two times better than previous work which proposed generalised predictors.

关键词： Accuracy Costs image coding Video on demand visual communication Computational modeling Machine learning Predictive models Video compression Video codecs

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Low-light image Enhancement Using Attention Mechanisms on Edge-Connect Architecture

Low-light Image Enhancement Using Attention Mechanisms on Ed...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Muhammet Nuri Dudak Büşra Söylemez Serdar Çiftçi Bilgisayar Mühendisliği Bölümü Harran Üniversitesi Şanlıurfa Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

images captured in low-light conditions often suffer from various issues such as noise, insufficient brightness, loss of sharpness, lack of detail, and color distortion. Although many efforts have been made to overcome these problems, the desired results have not yet been achieved. In this study, we investigated the effectiveness of enhancing low-light images by feeding the Edge-Connect architecture, which has shown successful results in the in-painting problem, with additional edge information, followed by using residual attention mechanisms in the intermediate layers. The experiments show that the best performance is achieved by using a Convolutional Block Attention Module in the residual layers. Compared to state-of-the-art methods, the proposed method improves the PSNR, SSIM, and LPIPS metrics, and the visual results are closer to the ground truth.

关键词： Measurement visualization Attention mechanisms image color analysis Convolution image edge detection Brightness

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Deep Reference Frame for Versatile Video Coding with Structural Re-parameterization

Deep Reference Frame for Versatile Video Coding with Structu...

引用

IEEE visual communications and image processing (VCIP)

作者： Chengzhuo Gui Yuantong Zhang Weijie Bao Zhenzhong Chen Huairui Wang Shan Liu Hubei Luojia Laboratory Wuhan University Wuhan China School of Geodesy and Geomatics Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In video coding, inter-prediction leverages neigh-boring frames to reduce temporal redundancy. The quality of these reference frames is essential for effective inter-prediction. Although many neural network-based methods have been proposed to improve the quality of reference frames, there is still room for the performance and efficiency trade-off. In this paper, we propose an interpolation diverse branch block (InterDBB) suitable for lightweight frame interpolation networks, which optimizes deep reference frame interpolation networks to improve performance without sacrificing speed and increasing complexity. Specifically, we propose a multi-branch structural reparameterization block without batch normalization. This straightforward yet effective modification ensures training stability and performance improvement. Moreover, we propose a parameterized motion estimation strategy based on different input resolution, to achieve a better trade-off between performance and computational complexity. Experimental results demonstrate that our method achieves -2.01%/-2.87%/-2.44% coding efficiency improvements for Y/U/V components under random access (RA) configuration compared to VTM-11.0_NNVC-5.0.

关键词： Video coding Training Interpolation image coding image resolution visual communication Motion estimation Redundancy Computational complexity Batch normalization

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Frame Level Content Adaptive λ for Neural Video Compression

Frame Level Content Adaptive λ for Neural Video Compression

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IEEE visual communications and image processing (VCIP)

作者： Zhirui Zuo Junqi Liao Xiaomin Song Zheng Liu Huiming Zheng Dong Liu University of Science and Technology of China Sichuan Xinshi Chuangwei Ultra HD Technology Co. Ltd

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural video compression (NVC) methods have made significant advances in recent years. In most NVC methods, all frames share the same Rate-Distortion trade-off parameter λ, which might be sub-optimal. Recently, inspired by traditional video codecs' hierarchical quality structure, DCVC-DC proposed allocating periodic weights to λ to equip NVC with the hierarchical quality structure. However, the inspiration from traditional video codecs is designed to complement their complex reference structure. Compared to traditional video codecs, NVC methods' reference structure is much simpler and may not require large fluctuations in their hierarchical quality structure. Moreover, DCVC-DC's fixed hierarchical quality structure ignored the influence of video content. We conduct an elaborate study on the hierarchical quality structure in DCVC-DC, shedding light on the potential for improving compression performance by proposing a content adaptive λ to achieve a more reasonable hierarchical quality structure based on the fixed hierarchical weights. Experimental results demonstrate that the proposed method achieves a better rate-distortion performance than allocating the fixed weights to the fixed λ. On DCVC-DC and DCVC-SDD, we achieved 4.9% and 8.8% bdrate reduction with our method.

关键词： Training image coding Fluctuations visual communication Rate-distortion Video compression Decoding Video codecs Computational complexity Periodic structures

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PointCU: Multiscale Sparse Convolutional Learning for Point Cloud Color Upsampling

PointCU: Multiscale Sparse Convolutional Learning for Point ...

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IEEE visual communications and image processing (VCIP)

作者： Birendra Kathariya Anique Akthar Zhu Li Geert Van Der Auwera University of Missouri-Kansas City Kansas City USA Qualcomm San Diego USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Upsampling a sparse point cloud is a common operation in various applications, such as high-quality reconstruction, rendering. Point-cloud geometry upsampling has been extensively studied for this purpose. However, when a sparse point cloud includes attributes, such as color, their upsampling must also be carried out in addition to geometry. Despite the apparent need of such work, not a lot of effective solutions are developed that can work with real world large scale point cloud. In this work, we propose a novel solution called PointCU, which is a sparse convolution learning-based Point cloud Color Upsampling method that enables high-fidelity dense point color reconstruction from sparse point color. The proposed method first prepares multiple representations of a dense point cloud through voxelization at different scales, then transfers the color from sparse to the newly created point clouds including the dense point cloud itself through devoxelization. Then by learning feature on these multiple point cloud representation through sparse convolution neural network (SparseCNN) while also expanding and fusing the feature to higher scale, PointCU achieves an excellent color super-resolving capability. Our experimental results on four times (4x) and eight times (8x) upsampling tasks demonstrate that the color upsampling performance of the proposed method is superior to the previous known color upsampling schemes by a large margin.

关键词： Point cloud compression Geometry Representation learning image resolution image color analysis Convolution visual communication Neural networks Color Rendering (computer graphics)

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Optimizing image Classification Using Bag of Features and Support Vector Machines

Optimizing Image Classification Using Bag of Features and Su...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International conference on

作者： K Mahantesh K S Navyashree Devika S Nairy R Asha B Anshitha Department of CSE (DS) RNS Institute of Technology Visvesvaraya Technological University Bengaluru India Department of ECE SJB Institute of Technology Visvesvaraya Technological University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. This work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector Machines (SVM). The BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. The suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications

关键词： Support vector machines visualization Histograms Computer vision Accuracy Computational modeling Scalability Feature extraction Vectors image classification

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ST-CLIP: Learning SpatioTemporal CLIP with GraphTransformer for Action Recognition

ST-CLIP: Learning SpatioTemporal CLIP with GraphTransformer ...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Zhenxing Zheng Jing Lyu Junlong Gong Gaoyun An School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China Institute of Information Science Beijing Jiaotong University Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Large pretrained models applied to image data have showcased significant progress in visual representation learning. However, the direct application of pretrained image models to video data is insufficient for capturing spatiotemporal context. The challenge lies in effectively transferring image-modal knowledge from web-scale data to the video domain. To enable spatiotemporal modeling, we propose SpatioTemporal CLIP (ST-CLIP) that incorporates a lightweight graph reasoning unit into each encoder of the vision Transformer to efficiently reason about temporal relations between frames. Extensive experiments and ablation studies highlight the competitiveness of ST-CLIP compared to state-of-the-art methods. The proposed model achieves impressive performance on fully-supervised experiments on Kinetics-400.

关键词： Representation learning visualization image recognition Neural networks Signal processing Transformers Data models Cognition Spatiotemporal phenomena Context modeling

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image-Based Indoor Localization Using Smartphone Cameras in Textureless Environment 6

Image-Based Indoor Localization Using Smartphone Cameras in ...

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6th International conference on Information and Computer Technologies, ICICT 2023

作者： Zheng, Ping Qin, Danyang Bai, Jianan Ma, Lin Heilongjiang University Department of Electronics and Communication Engineering Harbin China Heilongjiang University Harbin China Southeast University National Mobile Communications Research Laboratory Nanjing China Heilongjiang University Department of Information and Communication Engineering Harbin China

ISBN: (纸本)9798350300956

In recent years, Location Based Services (LBS) have developed rapidly. There is an urgent need to establish a low-cost and convenient method to get precise positions indoors. Therefore, we choose an easier way of using monocular camera mounted on a mobile terminal to achieve indoor visual positioning. In this paper, two main problems are studied under this condition. Firstly, the feature extraction method is designed to minimize the loss of positioning information while processing quickly in textureless environment. Secondly, the relative direction vector obtained from the epipolar geometry cannot converge at the point to be located due to errors, so a position determination method is proposed to transform the problem of positioning into distance calculation. A WeChat mini program was built for users to obtain location information. Experimental results show that 90% positioning error was less than 0. 5m, and the average error was less than 0.32m. © 2023 IEEE.

关键词： Location based services

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Attention-Based Deep Autoencoder for Hyperspectral image Denoising 1

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6th International conference on Computer Vision and image processing, CVIP 2021

作者： Kumar, Shashi Aetesam, Hazique Saha, Anirban Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Patna Patna801103 India

ISBN: (数字)9783031113499

ISBN: (纸本)9783031113482

Hyperspectral image (HSI) denoising is a crucial pre-processing task due to its widespread applications in areas that include geology, medicine, agriculture, surveillance, and the food industry. Denoising HSI data improves high-level vision tasks like classification, object tracking, and video surveillance. In this work, we propose a novel attention-based deep auto-encoder for HSI denoising which learns a mapping from noisy observation to clean the data. To exploit the spatial-spectral information present in HSI data, 3D symmetric convolution and deconvolution are used. The 3D features extracted from 3D convolutional layers (CL) helps the restoration across the depth dimension. However, feature maps obtained from the convolutional layers are localized and are not able to capture long-range dependencies in the data. Furthermore, CL does not differentiate between low and high degradation levels;producing smoothed results in low-textured areas of the image and artifacts in the high-textured regions. To avoid these adverse effects, attention blocks are plugged into the network to focus on more relevant features during the restoration process. Features obtained from attention blocks are fed as symmetric skip connections into the corresponding deconvolution layers. The proposed model is fully convolutional;it can process 3D images of arbitrary dimensions during training and inference. The experimental results show that our proposed model: Attention-based Deep Auto-encoder (AbDAE) outperforms the state-of-the-art methods in terms of visual as well as quantitative results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Dynamic Target Tracking Method of Surveillance Video Based on image processing

Dynamic Target Tracking Method of Surveillance Video Based o...

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communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Yiping Chen Fengshan Yuan Department of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China

In surveillance video, target tracking is an important part. Based on image processing technology, this paper studies a real-time and effective method to collect and recognize camera motion information. Firstly, the influence of visual dead angle and illumination on recognition is analyzed. Secondly, according to the characteristic of background light intensity, the corresponding algorithm is designed to realize the positioning and tracking control strategy of the target and surrounding environment scenery. Finally, the correctness of the method is verified by MATLAB simulation software, so as to obtain a better and scalable scheme, which is more economical and feasible after the occlusion rate is minimized.

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