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Kalman filter-based prediction refinement and quality enhancement for geometry-based point cloud compression

Kalman filter-based prediction refinement and quality enhanc...

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IEEE visual communications and image processing (VCIP)

作者： Lu Wang Jian Sun Hui Yuan Raouf Hamzaoui Xiaohui Wang School of Info. Sci. and Eng. Shandong University Qingdao China School of Contr. Sci. and Eng. Shandong University Jinan China School of Eng. and Sustain. Dev. De Montfort University Leicester UK

ISBN: (纸本)9781728173221

A point cloud is a set of points representing a three-dimensional (3D) object or scene. To compress a point cloud, the Motion Picture Experts Group (MPEG) geometry-based point cloud compression (G-PCC) scheme may use three attribute coding methods: region adaptive hierarchical transform (RAHT), predicting transform (PT), and lifting transform (LT). To improve the coding efficiency of PT, we propose to use a Kalman filter to refine the predicted attribute values. We also apply a Kalman filter to improve the quality of the reconstructed attribute values at the decoder side. Experimental results show that the combination of the two proposed methods can achieve an average Bjøntegaard delta bitrate of −0.48%, −5.18%, and −6.27% for the Luma, Chroma Cb, and Chroma Cr components, respectively, compared with a recent G-PCC reference software.

关键词： Point cloud compression image coding Three-dimensional displays visual communication Transform coding Transforms Motion pictures

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DVC-P: Deep Video Compression with Perceptual Optimizations

DVC-P: Deep Video Compression with Perceptual Optimizations

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IEEE visual communications and image processing (VCIP)

作者： Saiping Zhang Marta Mrak Luis Herranz Marc Górriz Blanch Shuai Wan Fuzheng Yang State Key Laboratory of Integrated Services Networks Xidian University Xi'an China BBC Research & Development The Lighthouse White City Place London UK Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728173221

Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this paper, we introduce deep video compression with perceptual op-timizations (DVC-P), which aims at increasing perceptual quality of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC) network, but improves it with perceptual optimizations. Specifically, a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate. Furthermore, nearest-neighbor interpolation is used to eliminate checkerboard artifacts which can appear in sequences encoded with DVC frameworks. Thanks to these two improvements, the perceptual quality of decoded sequences is improved. Experimental results demonstrate that, compared with the baseline DVC, our proposed method can generate videos with higher perceptual quality achieving 12.27% reduction in a perceptual BD- rate equivalent, on average.

关键词： Interpolation visualization image coding visual communication Bit rate Rate distortion theory Video compression

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Weighted Robinson Compass Gradient and Charbonnier Penalty Function as a Loss Function

Weighted Robinson Compass Gradient and Charbonnier Penalty F...

引用

Recent Advances and Innovations in Engineering (ICRAIE)

作者： Rachna U Dhruv Shindhe S S N Omkar Department of Electronics and Communications PES University Bangalore India ARTPARK Indian Institute Of Science Bangalore India Department of Aerospace Indian Institute Of Science Bangalore India

Loss function quantifies how well a machine learning model is performing, it computes the difference between the ground truth and the reconstructed outcomes. In image processing tasks such as image dehazing, image deblurring and image denoising the most popular loss functions used are Mean Squared error(MSE) and perceptual loss. MSE is less computationally expensive but fails to recreate precise boundaries whereas perceptual loss is more computationally expensive as they use feature vectors from a pre-trained model like VGG-net. In smaller on the edge systems like autonomous navigation systems, one of the most critical tasks is relying on visual cues, the take in the video feed from the environment and make decision on it after a few prepossessing steps to eliminate things like physical barriers, blurr, and smoke that clutter a robot’s environment are one of the several factors that affect autonomous navigation. We need systems that aren’t computationally expensive but also have a good reconstruction. For this we propose a loss function that incorporates gradients along with MSE for faster and better reconstruction.

关键词： Training visualization Technological innovation Computational modeling Mean square error methods image restoration Task analysis

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VATUN: visual Analytics for Testing and Understanding Convolutional Neural Networks

VATUN: Visual Analytics for Testing and Understanding Convol...

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23rd Eurographics conference on visualization, EuroVis 2021

作者： Park, Cheonbok Yang, Soyoung Na, Inyoup Chung, Sunghyo Shin, Sungbok Kwon, Bum Chul Park, Deokgun Choo, Jaegul NAVER Corp KAIST Skelter Labs Kakao Corp University of Maryland United States IBM Research University of Texas Arlington United States

ISBN: (纸本)9783038681434

Convolutional neural networks (CNNs) are popularly used in a wide range of applications, such as computer vision, natural language processing, and human-computer interaction. However, testing and understanding a trained model is difficult and very time-consuming. This is because their inner mechanisms are often considered as a 'black-box' due to difficulty in understanding the causal relationships between processes and results. To help the testing and understanding of such models, we present a user-interactive visual analytics system, VATUN, to analyze a CNN-based image classification model. Users can accomplish the following four tasks in our integrated system: (1) detect data instances in which the model confuses classification, (2) compare outcomes of the model by manipulating the conditions of the image, (3) understand reasons for the prediction of the model by highlighting highly influential parts from the image, and (4) analyze the overall what-if scenarios when augmenting the instances for each class. Moreover, by combining multiple techniques, our system lets users analyze behavior of the model from various perspectives. We conduct a user study of an image classification scenario with three domain experts. Our study will contribute to reducing the time cost for testing and understanding the CNN-based models in several industrial areas. © 2021 Eurographics Association. All rights reserved.

关键词： image classification

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TLRM: Task-level Relation Module for GNN-based Few-Shot Learning

TLRM: Task-level Relation Module for GNN-based Few-Shot Lear...

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IEEE visual communications and image processing (VCIP)

作者： Yurong Guo Zhanyu Ma Xiaoxu Li Yuan Dong Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9781728173221

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited labeled samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples' embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-imageNet, tiered-imageNet, CUB-200-2011, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning.

关键词： Measurement visual communication image processing Benchmark testing Graph neural networks Task analysis

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MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for image Classification 27th

MultiTune: Adaptive Integration of Multiple Fine-Tuning Mode...

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27th International conference on Neural Information processing

作者： Wang, Yu Plested, Jo Gedeon, Tom Australian Natl Univ Res Sch Comp Sci Canberra Australia

ISBN: (纸本)9783030638191;9783030638207

Transfer learning has been widely used as a deep learning technique to solve computer vision related problems, especially when the problem is image classification employing Convolutional Neural Networks (CNN). In this paper, a novel transfer learning approach that can adaptively integrate multiple models with different fine-tuning settings is proposed, which is denoted as MultiTune. To evaluate the performance of MultiTune, we compare it to SpotTune, a state-of-the-art transfer learning technique. Two image datasets from the visual Decathlon Challenge are used to evaluate the performance of MultiTune. The FGVC-Aircraft dataset is a fine-grained task and the CIFAR100 dataset is a more general task. Results obtained in this paper show that MultiTune outperforms SpotTune on both tasks. We also evaluate MultiTune on a range of target datasets with smaller numbers of images per class. MultiTune outperforms SpotTune on most of these smaller-sized datasets as well. MultiTune is also less computational than SpotTune and requires less time for training for each dataset used in this paper.

关键词： Transfer learning image classification Convolutional neural networks

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Object-Based Resolution Selection for Efficient Edge-Assisted Multi-Task Video Analytics

Object-Based Resolution Selection for Efficient Edge-Assiste...

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GLOBECOM 2022 - 2022 IEEE Global communications conference

作者： Chengzhi Wang Peng Yang Jie Lin Wen Wu Ning Zhang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Peng Cheng Laboratory Frontier Research Center Shenzhen China Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada

ISBN: (纸本)9781665435413

Camera-based monitoring is becoming increasingly popular, as multi-objective detection tasks can be enabled by video analytics over captured frames. Yet, video frames have to be delivered to computation-capable edge nodes for further processing, because the amount of required resources exceeds the capacity of built-in hardware of video cameras. In this paper, observing that video resolution directly determines the subsequent bandwidth and computing resource consumption, as well as the analytic accuracy, we propose an edge-assisted object-based resolution configuration algorithm to achieve efficient multi-task video analytics. The proposed algorithm harnesses the diversity of neural networks used for detecting different objects in one frame, which brings about two-fold possibility for bandwidth saving. On one hand, background information cannot be indiscriminately transmitted, as is unlikely to contribute to improving the analytics accuracy. On the other hand, fine-grained resolution selection allows object-level optimal resolution that minimizes the transmitted data volume under accuracy and latency constraints. Simulation results demonstrate that the proposed method can effectively reduce up to 50% of the transmitted data volume, compared to existing benchmarks.

关键词： Sensitivity visual analytics Simulation image edge detection Neural networks Bandwidth Multitasking

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NeCH: Neural Clothed Human Model

NeCH: Neural Clothed Human Model

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IEEE visual communications and image processing (VCIP)

作者： Sheng Liu Liangchen Song Yi Xu Junsong Yuan

ISBN: (纸本)9781728173221

Existing human models, e.g., SMPL and STAR, represent 3D geometry of a human body in the form of a polygon mesh obtained by deforming a template mesh according to a set of shape and pose parameters. The appearance, however, is not directly modeled by most existing human models. We present a novel 3D human model that faithfully models both the 3D geometry and the appearance of a clothed human body with a continuous volumetric representation, i.e., volume densities and emitted colors of continuous 3D locations in the volume encompassing the human body. In contrast to the mesh-based representation whose resolution is limited by a mesh's fixed number of polygons, our volumetric representation does not limit the resolution of our model. Moreover, our volumetric represen-tation can be rendered via differentiable volume rendering, thus enabling us to train the model only using 2D images (without using ground truth 3D geometries of human bodies) by minimizing a loss function which measures the differences between rendered images and ground truth images. On the contrary, existing human models are trained using ground truth 3D geometries of human bodies. Thanks to the ability of our model to jointly model both the geometries and the appearances of clothed people, our model can benefit applications including human image synthesis, gaming and 3D television and telepresence.

关键词： Geometry Video coding Solid modeling Three-dimensional displays image resolution Biological system modeling Volume measurement

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A Multi-dimensional Aesthetic Quality Assessment Model for Mobile Game images

A Multi-dimensional Aesthetic Quality Assessment Model for M...

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IEEE visual communications and image processing (VCIP)

作者： Tao Wang Wei Sun Xiongkuo Min Wei Lu Zicheng Zhang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

With the development of the game industry and the popularization of mobile devices, mobile games have played an important role in people's entertainment life. The aesthetic quality of mobile game images determines the users' Quality of Experience (QoE) to a certain extent. In this paper, we propose a multi-task deep learning based method to evaluate the aesthetic quality of mobile game images in multiple dimensions (i.e. the fineness, color harmony, colorfulness, and overall quality). Specifically, we first extract the quality-aware feature representation through integrating the features from all intermediate layers of the convolution neural network (CNN) and then map these quality-aware features into the quality score space in each dimension via the quality regressor module, which consists of three fully connected (FC) layers. The proposed model is trained through a multi-task learning manner, where the quality-aware features are shared by different quality dimension prediction tasks, and the multi-dimensional quality scores of each image are regressed by multiple quality regression modules respectively. We further introduce an uncertainty principle to balance the loss of each task in the training stage. The experimental results show that our proposed model achieves the best performance on the Multi-dimensional Aesthetic assessment for Mobile Game image database (MAMG) among state-of-the-art image quality assessment (IQA) algorithms and aesthetic quality assessment (AQA) algorithms.

关键词： Deep learning Uncertainty Games Feature extraction Multitasking Quality assessment Quality of experience

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Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

Improving Per-title Encoding for HTTP Adaptive Streaming by ...

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IEEE visual communications and image processing (VCIP)

作者： Hadi Amirpour Hannaneh Barahouei Pasandi Christian Timmerer Mohammad Ghanbari Christian Doppler Laboratory ATHENA Alpen-Adria-Universität Klagenfurt Austria Virginia Commonwealth University Richmond VA USA School of Computer Science and Electronic Engineering University of Essex Colchester UK

ISBN: (纸本)9781728173221

In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions, and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional upscaling approaches, and they are becoming more and more efficient over time. This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates, a maximum threshold for the quality is set, and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method.

关键词： image coding visual communication Bit rate Superresolution Bandwidth Streaming media Spatial resolution

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