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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for Criminal Investigation images 21

Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for...

引用

Proceedings of the 2021 4th International conference on Artificial Intelligence and Pattern Recognition

作者： Lan Rong Gao Xiaoge School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Criminal investigation image segmentation is considered one of the most important tasks in the field of criminal investigation image processing. However, the segmentation for criminal investigation images is a challenging task due to natural or human factors. In this paper, an image threshold segmentation algorithm based on fuzzy Kaniadakis entropy was proposed for criminal investigation image segmentation. Firstly, the weighted least squares filter was used for image pre-processing. Then, by using membership functions, image fuzzy sets were constructed based on restricted dissimilarity function. Finally, the maximum fuzzy Kaniadakis entropy was corresponded to the optimal segmentation threshold. The experimental results demonstrate that compared with several existing entropy-based thresholding algorithms, the proposed algorithm is effective in terms of visual effects and segmentation quality measures.

关键词： Criminal investigation image Fuzzy Kaniadakis entropy image thresholding segmentation

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Human Attention Based Movie Summarization: Dataset and Baseline Model

Human Attention Based Movie Summarization: Dataset and Basel...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Zhao, Defang Zhu, Dandan Min, Xiongkuo Yue, Jiaomin Zhang, Kaiwei Zhou, Qiangqiang Zhai, Guangtao Yang, Xiaokang CloudWalk Technology Shanghai China School of Computer Science and Technology Donghua University Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Software Jiangxi Normal University Nanchang China Ai Institute Shanghai Jiao Tong University MOE Key Lab of Artificial Intelligence Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The movie summarization model can automatically edit a condensed and succinct version of the movie by selecting the keyframes. Previous works mainly resort to hand-crafted heuristics and most of them are unsupervised. Supervised movie summarization is a new research field and, there is currently no publicly suitable dataset available. Moreover, existing works only focus on the movies themselves while neglecting the audiences, who have the most say in which part of the movie is more attractive. To deal with the aforementioned limitations, we establish a human attention based movie summarization dataset Movie50. Specifically, we explore the human attention variations when watching videos and have the following findings: (1) The attention of humans is concentrated when watching keyframes. (2) The attention of humans is distracted when watching non-keyframes. Inspired by these findings, we collect the eye fixations of 20 participants when watching 50 movies and propose a novel human attention based annotation pipeline. In addition, we introduce A/V-MSNet, an audiovisual neural network that takes advantage of spatio-temporal visual and auditory information to better model human attention as well as exploit more plentiful information. Extensive experiments demonstrate the superiority of the proposed method. © 2022 IEEE.

关键词： Motion pictures

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Random-access-aware Light Field Video Coding using Tree Pruning Method

Random-access-aware Light Field Video Coding using Tree Prun...

引用

IEEE visual communications and image processing (VCIP)

作者： Thuc Nguyen Huu Vinh Van Duong Byeungwoo Jeon Sungkyunkwan University South Korea

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The increasing prevalence of VR/AR as well as the expected availability of Light Field (LF) display soon call for more practical methods to transmit LF image/video for services. In that aspect, the LF video coding should not only consider the compression efficiency but also the view random-access capability (especially in the multi-view-based system). The multi-view coding system heavily exploits view dependencies coming from both inter-view and temporal correlation. While such a system greatly improves the compression efficiency, its view random-access capability can be much reduced due to so called "chain of dependencies." In this paper, we first model the chain of dependencies by a tree, then a cost function is used to assign an importance value to each tree node. By travelling from top to bottom, a node of lesser importance is cut-off, forming a pruned tree to achieve reduction of random-access complexity. Our tree pruning method has shown to reduce about 40% of random-access complexity at the cost of minor compression loss compared to the state-of-the-art methods. Furthermore, it is expected that our method is very lightweight in its realization and also effective on a practical LF video coding system.

关键词： Complexity theory Encoding Decoding Video coding Light fields image coding Correlation

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Advanced Learning-Based Inter Prediction for Future Video Coding

Advanced Learning-Based Inter Prediction for Future Video Co...

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IEEE visual communications and image processing (VCIP)

作者： Yanchen Zhao Wenhong Duan Chuanmin Jia Shanshe Wang Siwei Ma School of Computer Science Peking University China Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In the fourth generation Audio Video coding Standard (AVS4), the Inter Prediction Filter (INTERPF) reduces discontinuities between prediction and adjacent reconstructed pixels in inter prediction. The paper proposes a low complexity learning-based inter prediction (LLIP) method to replace the traditional INTERPF. LLIP enhances the filtering process by leveraging a lightweight neural network model, where parameters can be exported for efficient inference. Specifically, we extract pixels and coordinates utilized by the traditional INTERPF to form the training dataset. Subsequently, we export the weights and biases of the trained neural network model and implement the inference process without any third-party dependency, enabling seamless integration into video codec without relying on Libtorch, thus achieving faster inference speed. Ultimately, we replace the traditional handcraft filtering parameters in INTERPF with the learned optimal filtering parameters. This practical solution makes the combination of deep learning encoding tools with traditional video encoding schemes more efficient. Experimental results show that our approach achieves 0.01%, 0.31%, and 0.25% coding gain for the Y, U, and V components under the random access (RA) configuration on average.

关键词： Video coding Training image coding Filtering visual communication Computational modeling Neural networks Complexity theory Video codecs Standards

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IEEE visual communications and image processing (VCIP)

作者： Ziyin Huang Yui-Lam Chan Ngai-Wing Kwong Sik-Ho Tsang Kin-Man Lam Wing-Kuen Ling Department of Electronic and Information Engineering The Hong Kong Polytechnic University School of Information Engineering Guangdong University of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Compressed screen content videos often exhibit artifacts in edge areas and suffer from distortions during scene switches, where content abruptly changes between frames. Existing multi-frame models, which use a fixed range of neighbor frames, struggle with these switches. To address this, we propose a novel method that effectively handles scene switches. Our approach utilizes Long-term Feature Extraction (LFE) to capture contextual information, while the Frame Similarity-based Short-term Feature Extraction (FSFE) focuses on texture information to manage fast motion and scene switches. In FSFE, a Similarity-based Neighbor Frame Selector (SNFS) is designed to choose relevant neighbor frames for the short-term stream, enhancing the quality of scene switch frames. To fuse short-term and long-term features adaptively, we introduce a local-spatial and global-channel attention module, which recalibrates spatial and channel-wise feature responses. Experimental results show that our Frame Similarity-Based via Adaptive Long Short-Term Fusion (FSLST) method significantly improves the quality of compressed videos, outperforming current state-of-the-art methods.

关键词： image coding Fuses visual communication Streaming media Feature extraction Distortion Quality assessment Videos

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Two Fundamental Challenges in Perceptual Picture Codingand image Restoration

Two Fundamental Challenges in Perceptual Picture Codingand I...

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2nd IEEE conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

作者： Hong Ren WU Royal Melbourne Institute of Technology Australia

This paper examines two fundamental challenges in two areas, respectively, which have been intensively researched in the field of image processing and communications, i.e., digital picture coding/ compression and digital picture (including both video and still images) restoration (or de-noising). It reflects on historical developments and reviews the state-of-theart in the area of digital picture coding. Quantitative perceptual distortion measure based on the human visual system is identified as the weakest link in the current picture coding framework and remains a fundamental challenge in devising the next generation picture coding systems. In the area of picture restoration, the paper takes a special interest in surveillance and security video/image de-noising task for forensic investigations. Highlighting the most recent advances and the inadequacies in existing image de-noising techniques, it focuses on a fundamental challenge in designing a unified picture de-noising framework, including noise modeling, for removal of analogue and digital surveillance video distortions.

关键词： image coding image restoration Noise reduction Surveillance image denoising image processing Video compression Distortion measurement Current measurement Humans

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NeRV++: An Enhanced Implicit Neural Video Representation

NeRV++: An Enhanced Implicit Neural Video Representation

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IEEE visual communications and image processing (VCIP)

作者： Ahmed Ghorbel Wassim Hamidouche Luce Morin Univ Rennes INSA Rennes CNRS IETR Rennes France

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint. Though promising, INRs applied to video compression still need to improve their rate-distortion performance by a large margin, and require a huge number of parameters and long training iterations to capture high-frequency details, limiting their wider applicability. Resolving this problem remains a quite challenging task, which would make INRs more accessible in compression tasks. We take a step towards resolving these shortcomings by introducing neural representations for videos (NeRV)++, an enhanced implicit neural video representation (INVR), as more straightforward yet effective enhancement over the original NeRV decoder architecture, featuring separable conv2d residual blocks (SCRBs) that sandwiches the upsampling block (UB), and a bilinear interpolation skip layer for improved feature representation. NeRV++ allows videos to be directly represented as a function approximated by a neural network, and significantly enhance the representation capacity beyond current INR-based video codecs. We evaluate our method on UVG, MCL JVC, and Bunny datasets, achieving competitive results for video compression with INRs. This achievement narrows the gap to autoencoder-based video coding, marking a significant stride in INR-based video compression research. The code of NeRV++ is available on GitHub.

关键词： Video coding Training image coding Limiting visual communication Neural networks Rate-distortion Video compression Video codecs Software development management

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An Analog visual Feature Extraction Retina for Real-Time Stereo Vision

An Analog Visual Feature Extraction Retina for Real-Time Ste...

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European conference on Solid-State Circuits (ESSCIRC)

作者： Bogdan Arion Yang Ni Francis Devos Université Paris Sud Orsay France Institut National de Télécommunications Evry France

In this paper, we present an analog VLSI spatio-frequency analysis based visual feature extraction retina, dedicated to the real-time stereo vision. In this retina, local extreme points in the DoG filtered image are extracted as pertinent visual features. A 128-pixel line-based prototype chip is presented with experiment results. A processing speed of 100k lines/s has been obtained (exposition time excluded), it will be used in an integrated analog stereo vision system for real-time obstacle detection task.

关键词： Feature extraction Retina Stereo vision Band pass filters Switched capacitor networks MOS capacitors Very large scale integration Data mining Circuits Filtering theory

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Uniformly Accelerated Motion Model for Inter Prediction

Uniformly Accelerated Motion Model for Inter Prediction

引用

IEEE visual communications and image processing (VCIP)

作者： Zhuoyuan Li Yao Li Chuanbo Tang Li Li Dong Liu Feng Wu University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Inter prediction is a key technology in video coding to reduce the temporal redundancy. In natural videos, there are usually moving objects with changing velocity, resulting in complex motion fields that are difficult to represent compactly. In Versatile Video Coding (VVC), existing inter prediction methods usually assume uniform speed motion between consecutive frames, which may not well handle the complex motion fields in the real world. To address these issues, we introduce a uniformly accelerated motion model (UAMM) to exploit velocity and acceleration of moving objects between the video frames, and further combine them to assist in the inter prediction methods to handle the motion change in the temporal domain. First, we review the theory of UAMM. Second, we propose UAMM-based parameter derivation and extrapolation schemes in the coding process. Third, we integrate the UAMM into existing inter prediction modes (Merge, MMVD, CIIP) to achieve higher prediction accuracy. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 0.38% BD-rate reduction compared to the VTM anchor, under the Low-delay P configuration, with a slight increase of time complexity on the encoding/decoding side.

关键词： Video coding Accuracy image coding visual communication Reviews Motion estimation Redundancy Predictive models Software Time complexity

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HEp-2 cell classification using a deep neural network trained for natural image classification

HEp-2 cell classification using a deep neural network traine...

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IEEE Signal processing and communications Applications (SIU)

作者： Burak Benligiray Hatice Çınar Akakın Anadolu Universitesi Eskisehir TR

Deep convolutional neural networks is a recently developed method that yields very successful results in image classification. Deep neural networks, which have a high number of parameters, require a large amount of data to avoid overfitting during training. For applications in which the available data is not adequate to train a deep neural network from the scratch, deep neural networks trained for similar objectives can be used as a starting point. In this study, cell images are classified using a deep neural network trained to classify objects in natural images. Even though classification of natural images and cell images are very different objectives, cell images are able to be classified with 74.1% mean class accuracy. The results show that features used for visual classification by deep convolutional neural networks may be more universal than assumed.

关键词： Neural networks visualization Computer vision Training Pattern recognition conferences image recognition

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