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Reduced Reference Quality Assessment for Point Cloud Compression

Reduced Reference Quality Assessment for Point Cloud Compres...

引用

IEEE visual communications and image processing (VCIP)

作者： Yipeng Liu Qi Yang Yiling Xu Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

In this paper, we propose a reduced reference (RR) point cloud quality assessment (PCQA) model named R-PCQA to quantify the distortions introduced by the lossy compression. Specifically, we use the attribute and geometry quantization steps of different compression methods (i.e., V-PCC, G-PCC and AVS) to infer the point cloud quality, assuming that the point clouds have no other distortions before compression. First, we analyze the compression distortion of point clouds under separate attribute compression and geometry compression to avoid their mutual masking, for which we consider 5 point clouds as references to generate a compression dataset (PCCQA) containing independent attribute compression and geometry compression samples. Then, we develop the proposed R-PCQA via fitting the relationship between the quantization steps and the perceptual quality. We evaluate the performance of R-PCQA on both the established dataset and another independent dataset. The results demonstrate that the proposed R-PCQA can exhibit reliable performance and high generalization ability.

关键词： Point cloud compression Geometry image coding Quantization (signal) visual communication Fitting Distortion

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BAM: A Bidirectional Attention Module for Masked Face Recognition

BAM: A Bidirectional Attention Module for Masked Face Recogn...

引用

IEEE visual communications and image processing (VCIP)

作者： M. Saad Shakeel School of Automation Guangdong University of Petrochemical Technology Maoming China

ISBN: (纸本)9781665475938

Masked Face Recognition (MFR) is a recent addition to the directory of existing challenges in facial biometrics. Due to the limited exposure of facial regions due to mask-occlusion, it is essential to exploit the available non-occluded regions as much as possible for identity feature learning. Aiming to address this issue, we propose a dual-branch bidirectional attention module (BAM), which consists of a spatial attention block (SAB) and a channel attention block (CAB) in each branch. In the first stage, the SAB performs bidirectional interactions between the original feature map and its augmented version to highlight informative spatial locations for feature learning. The learned bidirectional spatial attention maps are then passed through a channel attention block (CAB) to assign high weights to only informative feature channels. Finally, the channel-wise calibrated feature responses are fused to generate a final attention-aware feature representation for MFR. Extensive experiments indicate that our proposed BAM is superior to various state-of-the-art methods in terms of recognizing mask-occluded face images under complex facial variations.

关键词： Representation learning image recognition visual communication Face recognition Computational modeling Performance gain Benchmark testing

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FPX-NVC: An FPGA-Accelerated P-frame Based Neural Video Coding System

FPX-NVC: An FPGA-Accelerated P-frame Based Neural Video Codi...

引用

IEEE visual communications and image processing (VCIP)

作者： Chuanmin Jia Xinyu Hang Wenbo Liu Shanshe Wang Siwei Ma Peking University Beijing China Beijing Boya Realscene Technologies Co. Ltd. Beijing China

ISBN: (纸本)9781665475938

Hardware-oriented neural video coding (NVC) has been an emerging research topic recently. In this work, we present a P-frame based NVC system by leveraging the parallel neural inference capability of resource-constrained embed platforms, such as field programmable gate array (FPGA). The proposed system, namely FPX-NVC, consists of multiple resolution video capture, reconfigurable and integerized neural video codec and display. Our neural codec enjoys the learned unidirectional inter-predictive coding by jointly optimizing the implicit motion representation (IMR), feature domain motion compensation, and the corresponding prediction error compression, realizing end-to-end on-board encoding and decoding. Our system supports end-to-end video data operation flow from capturing to neural coding and display. To our knowledge, the FPX-NVC is the first inter-predictive coding (P-frame) based hardware NVC system.

关键词： Video coding image coding visual communication Market research Hardware Motion compensation Systems support

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Genre Classification of Movie Trailers using Spectrogram Analysis and Machine Learning

Genre Classification of Movie Trailers using Spectrogram Ana...

引用

International Black Sea conference on communications and Networking (BlackSeaCom)

作者： Porawat Visutsak Fuangfar Pensiri Ponrudee Netisopakul Navapoom Punsathit Pongkorn Rojsuwan Maywadee Phuthong Worapol Khanthawat Wittawat Prommanee Kanthong Jearaphan Tawichai Saekua Tichaporn Anantaprueksa Napas Kavalee Naravitch Sukkokee Nanticha Kingket Prapassorn Teeravas Patchara Nimmonrat Patcharada Yungyuen Pattareeya Nurltes Ratchanon Wongpanti Sekson Hompangwhai Haritchaya Sutthikawee Apichai Siangchin Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand Department of Computer Science and Information Faculty of Science at Sriracha Kasetsart University Chonburi Thailand Faculty of Information Technology KMITL Bangkok Thailand

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

This study investigates the application of machine learning for movie trailer genre classification using spectrogram analysis. Spectrograms, visual representations of a signal's frequency content over time, are extracted from movie trailers. Convolutional Neural Networks (CNNs), known for their image recognition capabilities, are employed to analyze these spectrograms and identify patterns that distinguish between different genres. Our approach is compared to a Random Forest model for performance evaluation. The models are trained on a dataset of movie trailers categorized into five genres: action, romance, drama, comedy, and thriller. Librosa, a Python library, is utilized for audio pre-processing, while the overall training process is conducted within the Python environment. This paper explores the potential of machine learning in conjunction with spectrogram analysis for effective movie trailer genre classification.

关键词： Training Performance evaluation visualization Time-frequency analysis Humanities image recognition Motion pictures

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DE-CrossDet: Divisible and Extensible Crossline Representation for Object Detection

DE-CrossDet: Divisible and Extensible Crossline Representati...

引用

IEEE visual communications and image processing (VCIP)

作者： Hefei Mei Hongliang Li Heqian Qiu Jianhua Cui Longrong Yang University of Electronic Science and Technology of China

ISBN: (纸本)9781665475938

Object detection aims to localize and classify objects. Suitable object representation plays an important role in accurate detection. Because a complete crossline inevitably passes through the noise of backgrounds or other objects, object features directly extracted by the whole crossline are often confused. In this paper, we present a new feature extraction method, DE-Crossline, which can enhance the original crossline representation to capture more accurate object information. Specifically, we divide the crossline into several segments, each of which extracts the maximum activation key point respectively to reduce the impact of noise mentioned above. Furthermore, considering various shapes and sizes of objects, we design a Deformable Width Extension Module to learn a suitable width of each crossline, so as to capture richer object information. Extensive experiments prove the effectiveness of our proposed method. The total performance of our proposed detector can reach 49.0% AP, using ResNet-101 as backbone on the MS-COCO dataset.

关键词： image segmentation Shape visual communication Object detection Detectors Feature extraction Data mining

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Dual Attention Fusion Network for Single image Dehazing 13

Dual Attention Fusion Network for Single Image Dehazing

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Zhu, Hong Zhang, Dengyin Kou, Yingjie Nanjing University of Posts and Telecommunications School of Telecommunications and Informatica Engineering Nanjing210003 China

ISBN: (纸本)9781665407854

Recently, there have been significant advances in image dehazing methods based on the convolutional neural network. However, the convolutional kernel is a fixed receptive field. It considers that image features are equally weighted on channels and pixels, which leads to the loss of some vital feature information. In this paper, we propose an end-to-end Dual Attention Fusion Network(DAF-Net). The network consists of three residual groups, and each group comprises three residual dual attention fusion modules. The module consists of a residual block and channel and pixel attention fusion to strengthen the global dependence and obtain pixel characteristics. Based on the different features of the fusion modules, adaptive learning is performed using channel and pixel attention to give more weight to essential features. This model structure also keeps the shallower information and transfers it to the deeper layers. The experimental results show that DAF-Net retains details and improves the visual effects by using a stack of fewer residual dual attention fusion modules on synthetic and real-world images. © 2021 IEEE.

关键词： image enhancement

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SparseARFM-SI: Rotary Point Cloud Place Recognition Based on Multi-Resolution and Attention Mechanism

SparseARFM-SI: Rotary Point Cloud Place Recognition Based on...

引用

IEEE visual communications and image processing (VCIP)

作者： Xiaohang Liu Ruonan Zhang Jie Wang Ge Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China School of Software Dalian University of Technology Dalian China

ISBN: (纸本)9781665475938

Place recognition based on 3D point cloud can directly describe the scene of 3D world by acquiring 3D point clouds via LiDAR, which is robust with environmental changes. The core challenge locates at how to obtain compact and representative feature expression for positioning. In this paper, SparseARFM-SI framework is proposed to solve the problem of feature extraction caused by the large size difference of different objects and the difficulty of feature extraction based on the point cloud data collected by rotating LiDAR. SparseARFM-SI is mainly composed of four models: data representation model, sparse convolution model, ARFM model and NetVLAD model. Experiments show that SparseARFM-SI framework has good performance in USyd data set collected based on rotating Lidar.

关键词： Point cloud compression Solid modeling Laser radar Three-dimensional displays image recognition visual communication Convolution

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On Data Annotation Efficiency for image Based Crowd Counting

On Data Annotation Efficiency for Image Based Crowd Counting

引用

IEEE visual communications and image processing (VCIP)

作者： Tianfang Ma Shuoyan Liu Qian Wang Tianjin University China National University of Singapore Durham University UK

ISBN: (纸本)9781665475938

Crowd counting aims at automatically estimating the number of persons in still images. It has attracted much attention due to its potential usage in surveillance, intelligent transportation and many other scenarios. In the recent decade, most researchers have been focusing on the design of novel deep learning models for improved crowd counting performance. Such attempts include proposing advanced architectures of deep neural networks, using different training strategies and loss functions. Other than the capabilities of models, the crowd counting performance is also determined by the quantity and the quality of training data. Whilst the deep models are data-hungry and better performance can usually be expected with more training data, annotating images for training is time-consuming and expensive in real-world applications. In this work, we focus on the efficiency of data annotation for crowd counting. By varying the number of annotated images and the number of annotated points (one point is annotated per person head) for training, our experimental results demonstrate it is more efficient to annotate a small number of points per image across a large number of images for training. Based on this conclusion, we present a novel adaptive scaling mechanism for data augmentation to diversify the training images without extra annotation cost. The mechanism is proved effective via thorough experiments.

关键词： Training Deep learning Annotations visual communication Surveillance image processing Neural networks

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MVCD: Multi-Dimensional Video Compression Dataset

MVCD: Multi-Dimensional Video Compression Dataset

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IEEE visual communications and image processing (VCIP)

作者： Hadi Amirpour Mohammad Ghasempour Farzad Tashtarian Ahmed Telili Samira Afzal Wassim Hamidouche Christian Timmerer Christian Doppler Laboratory ATHENA Alpen-Adria-Universitat Klagenfurt Austria Technology Innovation Institute Masdar City Abu Dhabi UAE INSA Rennes CNRS Univ. Rennes Rennes France

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In the field of video streaming, the optimization of video encoding and decoding processes is crucial for delivering high-quality video content. Given the growing concern about carbon dioxide emissions, it is equally necessary to consider the energy consumption associated with video streaming. Therefore, to take advantage of machine learning techniques for optimizing video delivery, a dataset encompassing the energy consumption of the encoding and decoding process is needed. This paper introduces a comprehensive dataset featuring diverse video content, encoded and decoded using various codecs and spanning different devices. The dataset includes 1000 videos encoded with four resolutions (2160p, 1080p, 720p, and 540p) at two frame rates (30fps and 60fps), resulting in eight unique encodings for each video. Each video is further encoded with four different codecs — AVC (libx264), HEVC (libx265), AV1 (libsvtav1), and VVC (VVenC) — at four quality levels defined by QPs of 22, 27, 32 and 37. In addition, for AV1, three additional QPs of 35, 46 and 55 are considered. We measure both encoding and decoding time and energy consumption on various devices to provide a comprehensive evaluation, employing various metrics and tools. Additionally, we assess encoding bitrate and quality using quality metrics such as PSNR, SSIM, MS-SSIM, and VMAF. All data and the reproduction commands and scripts have been made publicly available as part of the dataset, which can be used for various applications such as rate and quality control, resource allocation, and energy-efficient *** URL: https://***/cd-athena/MVCD.

关键词： Video coding Energy consumption image coding Quality control Streaming media Encoding Decoding Resource management Video codecs Optimization

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Invertible image Dataset Protection

Invertible Image Dataset Protection

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Chen, Kejiang Zeng, Xianhan Ying, Qichao Li, Sheng Qian, Zhenxing Zhang, Xinpeng University of Science and Technology of China China Fudan University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The security of data storage is a big issue for companies. They must take effective steps to prevent valuable image datasets from being stolen for illegal commercial purposes. While data encryption is a common solution, it drastically down-grades the visual quality and therefore forbids common yet trivial use such as eye-checking without a decryption. We present a novel solution for dataset protection in this scenario by robustly and reversibly transform the images into adver-sarial images. An invertible image Dataset Protection NET-work (IDP-Net) is developed to introduce slight and acceptable changes to the images within the dataset. The protected images can be published and circulated on the social networks instead of their original version. Malicious attackers can only observe the images but cannot train pirated models based on them. Meanwhile, IDP-Net ensures the performance of au-thorized models, namely, trusted users can revert the protection and retrieve the protected images to their original version. Therefore, the dataset can be stored within the pro-tected version alone to ensure safety. Extensive experiments demonstrate that IDP-Net can better protect the security of image dataset against defensive methods compared to previ-ous methods. Besides, the introduced distortion is acceptable and the original images can be reconstructed nearly error-free. © 2022 IEEE.

关键词： Digital storage

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