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Compressed Video Quality Metric Based on Just-Noticeable-Difference and Saliency-aware Blocking Detection 7

Compressed Video Quality Metric Based on Just-Noticeable-Dif...

引用

7th International conference on Computer and communications, ICCC 2021

作者： Wang, Zheng Liao, Guodong Wu, Yuxuan Lin, Liqun Yang, Jing Zheng, Haifeng Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Huawei Technologies Co.Ltd. Guangdong Shenzhen China

ISBN: (纸本)9781665409506

Block-based prediction-quantization hybrid coding framework is widely used in video compression, which results in visually annoying artifacts, i.e., blocking artifacts, on encoded videos. They significantly degrade the Quality-of-Experience (QoE). To improve the QoE, it is crucial to develop Video Quality Assessment (VQA) methods that can identify and quantify various artifacts, among which the negative effect introduced by blocking artifacts is of guiding significance. To evaluate and monitor video quality effectively, a novel Blocking-based Compressed Video Quality Assessment (BCVQA) is proposed in this paper. The key idea of the BCVQA method is a blocking detection model based on Just-Noticeable-Difference (JND) mechanism, which is aimed to improve detection performance, since it only focuses on the observable blocking artifacts. Furthermore, the saliency model is utilized in quality prediction procedure after blocking detection, considering the visual attention mechanism. With the fusion of the above two visual mechanisms, quality prediction of compressed videos becomes more effective, since the useful information is refined in our framework. Experimental results show that the proposed BCVQA is consistent with perceived quality and can effectively evaluate video quality. © 2021 IEEE.

关键词： image compression

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Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in visual Cognitive Development 5th

Fusing Knowledge and Experience with Graph Convolutional Ne...

引用

5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Meituan-Dianping Group Beijing100190 China

ISBN: (纸本)9789811623356

visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image recognition

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Compressed sensing for SAR image reconstruction 2

Compressed sensing for SAR image reconstruction

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2nd IEEE International conference on Electronic Engineering, ICEEM 2021

作者： El-Ashkar, Alaa M. Shendy, H. El-Shafai, Walid Taha, Taha El Sayed El-Fishawy, Adel S. El-Nabi, Mohamed Abd El-Samie, Fathi E. Abd Menoufia University Faculty of Electronic Engineering Communications and Electronics Department Menouf Egypt

ISBN: (纸本)9781665418423

Synthetic Aperture Radar (SAR) is a common radar imaging technique in a wide range of applications. The SAR imaging relies on keeping eye on targets and imaging from various angles by synchronizing the movement of antenna with the target of interest. The large size of SAR images compared with storage hardware limitations or connection capacity limitations introduced a need of using compression techniques. The primary propose of this paper is to introduce an effective compression technique that can achieve high compression rates, while retaining critical information without damage or loss. Compressed Sensing (CS) represents a reliable, highly dependable and effective choice. This paper introduces a CS technique for SAR image reconstruction. The proposed technique addresses the issue of storing or transmitting large-size SAR images over restricted links, while preventing quality degradation produced by processing. Both visual and numerical results indicate the success and reliability of the presented technique. © 2021 IEEE.

关键词： Radar imaging

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A Deep Pyramid Attention Network for Single image Super-resolution 9

A Deep Pyramid Attention Network for Single Image Super-reso...

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9th International Japan-Africa conference on Electronics, communications, and Computations, JAC-ECC 2021

作者： Gendy, Garas Mohammed, Hazem Sabor, Nabil He, Guanghui Shanghai Jiao Tong University Department of Micro-Nano Electronics Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut Egypt Assiut University Faculty of Engineering Electrical Engineering Department Assiut Egypt

ISBN: (纸本)9781665482929

The pyramid attention (PA) network is a new structure developed for digital image processing. This network was designed to extract long-range features at different locations and scales. Recently, PA architecture has been introduced in single image super-Resolution (SISR) to improve the model's ability to benefit from data's self-similarity. However, the effects of location and number of PA on extracting the self-similarity are not explored. In this paper, a Deep Pyramid Attention Network (DPANet) is proposed for SISR based on exploring the PA block. This is performed by studying the effect of varying the number of PA blocks and their locations on the model performance. Moreover, the effect of the residual scale on the PA's performance is studied. Evaluated based on five benchmark datasets, we concluded that using five PA blocks without down-scale residual interchanging with Resblocks in the network achieves significantly better results compared to the state-of-the-art methods. In addition, our model achieves superior visual quality and accuracy. © 2021 IEEE.

关键词： Location

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Using Regularity Unit As Guidance For Summarization-Based image Resizing

Using Regularity Unit As Guidance For Summarization-Based Im...

引用

IEEE visual communications and image processing (VCIP)

作者： Fang- Tsung Hsiao Yi-Hsien Lin Yi-Chang Lu National Taiwan University Taipei Taiwan

ISBN: (纸本)9781728173221

In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.

关键词： Three-dimensional displays visual communication image processing Lighting Optimization

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A Deep Learning-based Audio-in-image Watermarking Scheme

A Deep Learning-based Audio-in-Image Watermarking Scheme

引用

IEEE visual communications and image processing (VCIP)

作者： Arjon Das Xin Zhong University of Nebraska Omaha

ISBN: (纸本)9781728173221

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.

关键词： Resistance Deep learning visual communication image processing Semantics Neural networks Watermarking

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Smart Sorting: Enhancing Almond Quality with DenseNet-201-Based image Classification

Smart Sorting: Enhancing Almond Quality with DenseNet-201-Ba...

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2024 International conference on Information Science and communications Technologies, ICISCT 2024

作者： Kaur, Arpanpreet Mishra, Amit Kumar Kumar, Bura Vijay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527921

Considered as two varieties: damaged and undamaged, almonds are a rather consumed agricultural product. Maintaining consumer satisfaction and industry standards depends on good detection of damaged almonds., which could be influenced by mechanical damage., mold, or pests. On the other hand, slow, labor-intensive hand inspection methods prone to mistake demand automated classification systems. This work meets that demand by automating the categorization process using DenseNet-121, a convolutional neural network (CNN), therefore lowering labor expenses and increasing accuracy. Particularly considering the tiny dataset of 1,000 photos split equally between damaged and undamaged almonds, DenseNet-121's architecture-with its dense connections and capacity to reuse features-is well-suited for the almond classification challenge. Pre-trained on imageNet and polished for this particular purpose, the model-trained across 60 epochs using a 32-batch size-achieved an accuracy of 95.27%. Random horizontal flips, resizing., and center cropping among other data augmentation techniques improved the model's generalizing capability. This work demonstrates the model's ability to substitute human methods in actual environments by showing how well it employs little visual information to classify almonds. Automating almond sorting speeds and provides higher consistency in quality control as well as efficiency of sorting. This work opens the road for further research of machine learning techniques in agricultural processing with possible applications in food quality assurance. © 2024 IEEE.

关键词： Damage detection

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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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High-level image representation-based on Gestalt theory for image classification 1

High-level image representation-based on Gestalt theory for ...

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1st International conference on communications, Control Systems and Signal processing, CCSSP 2020

作者： Sabrina, Aiouez Bouzar Feryel, Souami Khadidja, Belattar University of Science and Technology Houari Boumediene Computer Science Department Algiers16000 Algeria College of NTIC Constantine University 2 Computer Science Department Constantine25000 Algeria

ISBN: (纸本)9781728158358

Feature extraction and representation are crucial stages for image classification. However, the derived features might not be robust discriminators, which radically alter image classification results. In this paper, we present an enhanced Bag of visual Words representation for better image classification. We are inspired by the Gestalt laws of ***, our design goals are:•Introduce the spatial information into Bag of Words representation by grouping the aligned visual words (high order features), •Preserve the spatial relationships among the visual words groups against different geometric transformations, •Achieve the high-level representation of visual words by extracting visual phrases. Theses phrases convey the semantic meaning of object *** proposed approach has been evaluated, in terms of classification accuracy, on Caltech101 image database. Experimental results indicate that the classification performances are improved by using visual phrases. © 2020 IEEE.

关键词： image classification

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An efficient algorithm for cancelable biometric recognition based on noise magnification 2

An efficient algorithm for cancelable biometric recognition ...

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2nd IEEE International conference on Electronic Engineering, ICEEM 2021

作者： Farouk Al-Libady, Abd El-Rahman Ashiba, Huda El Banby, Ghada M. El-Fishawy, Adel S. Dessouky, Moawad I. Abd El-Samie, Fathi E. El-Rabaie, El-Sayed M. Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Egypt Zagazige University Bilbis Higher Institute of Engineering Department of Electronics and Electrical Communications Engineering Egypt Menoufia University Faculty of Electronic Engineering Department of Industrial Electronics and Control Engineering Egypt Menoufia University Faculty of Electronic Engineering Communications and Electronics Department Menouf Egypt Manoufia University: Menoufia University Faculty of Electronic Engineering Communications and Electronics Department Egypt

ISBN: (纸本)9781665418423

More recently, biometric systems have spread for modern security applications. Unfortunately, these systems have experienced several attempts of hacking. If biometric databases are compromised and stolen, biometrics in these databases will be lost forever. Consequently, there is an immediate need to introduce new upgradable biometric systems. The concept behind cancelable biometrics is to convert biometric data to alternative templates, which cannot be easily used by the impostor or intruder, and can be eliminated if breached. In this paper, the inverse filter is utilized in a cancelable face recognition system. In this system, masked biometric images are generated by blurring, noise addition and then inverse filtering. It is well-known in the image processing theory that inverse filtering leads to noise enhancement, which is an undesired effect in image restoration. In contrary, this effect will be desired in cancelable biometric systems. If the noise is magnified with an appropriate extent, it can mask the original biometrics leading to cancelable templates. This is the theory behind the proposed system. The proposed system is applied on the Olivetti and Oracle (ORL) dataset. Simulation results using evaluation metrics such as non-invertibility, unlinkability, visual inspection, False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Decidability, correlation coefficient, Area under the Receiver Operating Characteristic (AROC) curve demonstrate that the proposed system is resistant to intruders and hackers. Hence, it is efficient for several security applications. © 2021 IEEE.

关键词： Biometrics

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