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

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Compression of HDTV signals for low bit-rate transmission using motion compensated subband transform coding and a self-organization neural network

Compression of HDTV signals for low bit-rate transmission us...

引用

Data Compression conference (DCC)

作者： R. Bhaskaran S.C. Kwatra Department of Electrical Engineering University of Tolctlo and Eötvös Lárand University Toledo OH USA

The quality of visual communications has greatly improved with HDTV. Due to the advantages of digital communications, and the trend towards it, it is desirable to develop a cost effective digital codec for HDTV signals. One of the criteria in the transmission of digital signals via satellites is the transmission bandwidth which dictates the transmission costs. Apart from development of good compression schemes, due consideration should also be given to the ease of hardware implementation, which is crucial in deciding factors like processing delay and the level of encoder/decoder complexity. With the above mentioned factors in mind a cost-effective HDTV codec is proposed. Few US organizations have addressed the problem of HDTV distribution via communication satellites. The majority of efforts in the development of an HDTV codec have been aimed at the 100 to 140 Mbits/sec data rate. To facilitate cost effective satellite transmission, the DS3 rate of 45 Mbits/sec, is desirable. The purpose of the present research is to compress the HDTV signals to around 20 Mbits/sec so that 2 HDTV channels can be transmitted through the DS3 rate channel. To achieve this degree of compression a new scheme is proposed. First subband coding is used to decompose the image into different frequency bands. Next motion compensation is applied to the low band, and the resulting interframe difference is discrete cosine transform (DCT) coded. The high bands are also DCT coded. The DCT coefficients of the difference signal of the low band and the DCT coefficients of the high band are then vector quantized.< >

关键词： HDTV Discrete cosine transforms Costs Codecs image coding Satellites visual communication Digital communication Bandwidth Hardware

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Partial distortion based computation-constraint motion estimation

Partial distortion based computation-constraint motion estim...

引用

International conference on Consumer Electronics, communications and Networks

作者： Chi-Kao Chang Min-Yuan Fang I-Chang Jou Ming-Haw Jing Chung-Ming Kuo Department of Information Engineering I-Shou University Kaohsiung Taiwan

ISBN: (纸本)9781457714146

Recently, the rapid development of wireless networks and multimedia communication techniques provide solutions to achieve watching digital videos on hand-held devices. Motion estimation (ME), the most important part of video coding, has been used to increase the effectiveness of video compression. However, the computations of motion estimation could be failed in limited computing environments even using the fast algorithms. Therefore, the traditional ME algorithms cannot directly applied to the hand-held devices. In this paper, we propose a partial distortion scheme that sacrifice little visual quality for hand-held devices in a given computation constraint condition. We also analyze various computation-aware based block matching algorithms and present an efficient method to compute the motion vectors. The proposed method significantly reduces the requirement of computation power in computing motion vectors while the number of search points needed is only 25% of the conventional method.

关键词： Motion estimation Algorithm design and analysis Videos PSNR Prediction algorithms Signal processing algorithms image coding

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2004 IEEE International conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763)

2004 IEEE International Conference on Multimedia and Expo (I...

引用

IEEE International conference on Multimedia and Expo (ICME)

The following topics are dealt with: image processing and classification; multimedia coding; face, gesture and body analysis; active learning for multimedia information retrieval; advanced video coding techniques for HDTV and video applications; multimedia semantics; 3D graphics processing; QoS, rate control, and broadcasting; multimedia retrieval; video coding; emerging multimedia security technologies for digital rights management; multimedia streaming; motion estimation; video segmentation, summarization, and structuring; layered, scalable and multiple description transmission; multimedia architecture and video surveillance; media identification; browsing large multimedia collections; content-based retrieval; multimedia indexing and representation; virtual reality; content protection; smart multimedia acquisition systems; multimedia learning; multimedia compression; multi-modality-based media semantic analysis; noise removal; face, scene, human, and song recognition; multimedia editing and authoring; video streaming; speech and audio processing/recognition; error concealment for media transmission; multimedia distribution; visual detection and tracking; video object detection and analysis; multimedia transcoding; mobile imaging; wireless multimedia techniques; human-machine interface; watermarking, fingerprinting and authentication; automatic indexing; multimedia communications/networking; content based music structure analysis; video summarization; content understanding and transcoding techniques for media adaptation; interactive TV.

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Deep Learning: A Detailed Analysis Of Various image Augmentation Techniques

Deep Learning: A Detailed Analysis Of Various Image Augmenta...

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Networking and communications (ICNWC), International conference on

作者： S. Swathi M. Rajalakshmi Vijayalakshmi Senniappan Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India Department of Information Technology Coimbatore Institute of Technology Coimbatore India Department of Information Technology Park College of Engineering & Tech Coimbatore India

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses.

关键词： Deep learning Surveys Computer vision visualization Machine learning algorithms Taxonomy Feature extraction

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A Hybrid CNN-Tree Based Model for Enhanced image Classification Performance

A Hybrid CNN-Tree Based Model for Enhanced Image Classificat...

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IEEE Signal processing and communications Applications (SIU)

作者： Musa Aydın Zeki Kuş Zeliha Kaya Akçelik Fatih Sultan Mehmet Vakıf Üniversitesi Bilgisayar Mühendisliği ˙Istanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Blood cells play an essential role in various bodily functions, such as protection against infections and the body’s defense. The accurate classification of blood cells, generally grouped as red, white, and platelets is important for clinical diagnosis and hematological analysis. However, identifying these cells is a specialized and time-consuming process. Therefore, there is a hot-topic for high-precision automatic blood cell classification methods. Convolutional neural networks (CNNs) are a deep learning model used for visual data analysis and are very powerful in extracting features from data. In this study, we propose a hybrid classification model that combines the feature extraction power of CNNs with the ensemble-based prediction capabilities of Random Forest and XGBoost algorithms. The proposed hybrid model is compared with different methods on the BloodMNIST dataset in terms of classification performance and inference time. The results show that the tree-based methods outperform CNN by up to 8.49 and 11.62 points and achieve up to 82.9 times better inference times than other methods.

关键词： visualization Signal processing algorithms Cells (biology) Signal processing Predictive models Feature extraction Prediction algorithms

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MVCD: Multi-Dimensional Video Compression Dataset

MVCD: Multi-Dimensional Video Compression Dataset

引用

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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Video Anomaly Detection Using Open Data Filter and Domain Adaptation

Video Anomaly Detection Using Open Data Filter and Domain Ad...

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IEEE visual communications and image processing (VCIP)

作者： Chen Zhang Guorong Li Li Su Weigang Zhang Qingming Huang School of Computer Science and Technology UCAS Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Harbin Institute of Technology Weihai China Key Lab of Intelligent Information Processing ICT CAS Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Video anomaly detection is a very challenging task because of the rarity, openness, and the definition of the anomalies. Researchers pay more attention to the characteristics of anomalies and have proposed a variety of anomaly detection models. However, most existing methods only use normal events to construct anomaly detection models and ignore the diversity and openness of normal events. Actually, because real-world video data often have an open-ended distribution, some normal patterns hardly ever appeared in the training data. In addition, analogous to human experience in identifying anomalies, rare abnormal events can play a certain role in the detection of similar abnormal events in the dataset. Therefore, assuming that a small number of abnormal events are known, we propose a novel supervised anomaly detection model which explicitly detects open normal events and open abnormal events in the dataset and treats open data and seen data with different classifiers. First, we use the training video to train an imbalanced classifier as the seen data classifier. Then, during the testing phase, an open data filter module isused to divide the test data into seen data and open data. Finally, we directly use the seen data classifier to generate anomaly scores for the seen test data. For the open test data, we adopt a domain adaptation method to reduce the distribution difference between it and the training data and train a new classifier to score for it. Extensive experimental results prove the effectiveness of our model.

关键词： Training Training data Anomaly detection Feature extraction Data models Testing Adaptation models

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The boundless spaces of expanded multimedia

The boundless spaces of expanded multimedia

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International conference on Systems, Signals and image processing, IWSSIP

作者： Aleksandar Todorović University of the Arts Belgrade Serbia Mediterranean University Podgorica Montenegro

Back in 1970 Gene Youngblood published his book “Expanded Cinema”, undoubtedly one of the major milestones in the world of media theory. In the book he argues that during the late 1960 cinema and the whole complex of audiovisual media, reached a tipping point when the boundaries of the world of moving images broke down and a new visual reality emerged. That was the time when the videosphere appeared and the intermedia where born. Today, forty years later, we see another, somewhat similar event. The intermedia, which in the meantime evolved in multimedia, reached another “Seldon crisis point”, and we can witness how the whole complex of multimedia creation, processing, distribution and consumption is undergoing a radical change. That change shall profoundly modify the world of mass media communications. The boundaries that until now separated different mass media are becoming progressively blurred and in a not so distant future they might completely disappear creating thus just one big media industry. In a surrogate social environment lonely media consumers will exchange interaction for communication and try to escape the harsh reality by immersing themselves in infinite virtual worlds of the boundless expanded multimedia.

关键词： Multimedia communication Advertising image coding

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A Deep Learning Based System for Automatic Sorting and Quality Grading of Citrus Fruits

A Deep Learning Based System for Automatic Sorting and Quali...

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International conference on Emerging Trends in Networks and Computer communications (ETNCC)

作者： Nidhi Kundu Sonam Geeta Rani Vijaypal Singh Dhaka Computer Science Sri Karan Narendra Agriculture University Jobner Jaipur India Computer Science Engg. Manipal University Jaipur Jaipur India Computer and Communication Engg. Manipal University Jaipur Jaipur India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The citrus industry plays a pivotal role in the global agricultural sector, contributing significantly to the economy. Approximately 161 million tonnes of citrus fruits were produced worldwide. One third of the total production is used in processing industry. The price of citrus fruits is highly dependent on its quality parameters such as size, citrus color index, maturity index, diseases etc. Therefore, grading and sorting of citrus fruits based on the quality parameters is demand of the market to maximize the profit. Manual grading and sorting are labour-intensive, time-consuming, and often prone to human error. Automatic grading and sorting of citrus fruits can resolve the above-stated issues. In this paper, we utilize the pattern recognition and image processing potential of deep learning techniques and propose a deep learning based automated system to revolutionize the citrus fruits grading process. The system utilizes Convolutional Neural Networks (CNNs) to extract high-level features from lemon, orange images, allowing for accurate classification based on quality parameters such as size, color, and visual defects. We curated a dataset comprising diverse lemon, orange samples from Kaggle to evaluate the performance of the developed model. Additionally, our system incorporates real-time processing capabilities, ensuring a swift and continuous flow of fruits through the sorting process. Thus, it offers a robust and scalable solution for the citrus industry to enhance profit in the global citrus market.

关键词： Industries Deep learning visualization Accuracy image color analysis Real-time systems Convolutional neural networks Reliability Indexes Sorting

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A Completed Gaussian Extended Binary Pattern for Texture image Classification 23

A Completed Gaussian Extended Binary Pattern for Texture Ima...

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Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Xiaochun Xu Weizheng Lin Dingrong Chen Nanling Su Leinuo Tang Changxu Cai College of Computer and Control Engineering Minjiang University China International Digital Economy College Minjiang University China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance.

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