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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

共 4633 条记录，以下是381-390 订阅

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KonIQ-10k-LT: Overcoming Score Priors in Blind image Quality Assessment Under Imbalanced Distributions

KonIQ-10k-LT: Overcoming Score Priors in Blind Image Quality...

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IEEE visual communications and image processing (VCIP)

作者： Desen Yuan Lei Wang University of Electronic Science and Technology of China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Blind image Quality Assessment (BIQA) is essential in computational vision for predicting the visual quality of digital images without reference counterparts. Despite advancements through convolutional neural networks (CNNs), a significant challenge in BIQA remains the long-tail distribution of image quality scores, leading to biased training and reduced model generalization. To address this, we restructured the KonIQ-10k dataset to create an imbalanced version named KonIQ-10k-LT, manipulating the distribution of image quality scores to have opposing distributions in the training and validation sets. This restructuring increases the proportion of certain quality scores in the training set while decreasing them in the validation set. Experimental results show a significant performance decline of BIQA models on the KonIQ-10k-LT dataset compared to the original KonIQ-10k, highlighting the challenge posed by the long-tail distribution. To mitigate this issue, we propose a Proportion Weighted Balancing (PWB) method as a baseline, designed to enhance the robustness and generalization ability of BIQA models. Our findings demonstrate that the proposed WB method improves the performance and reliability of BIQA models under these challenging conditions.

关键词： image quality Training visualization Heavily-tailed distribution visual communication image processing Digital images Computational modeling Robustness Convolutional neural networks

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Effect of latency on social presence in traditional video conference and VR conference: a comparative study

Effect of latency on social presence in traditional video co...

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IEEE visual communications and image processing (VCIP)

作者： Shengnan Wang Jiarun Song Anthony Trioux Miao Wang Yusong Gao Fuzheng Yang School of Telecommunications Engineering Xidian University Xi’an China

Virtual reality (VR) conference, as a typical social VR application, has gained popularity in recent years. It offers users located at different locations a fully immersive experience and a sense of togetherness. However, the remote communication also introduces inevitable latencies, which may adversely affect the so-called social presence. There is still a lack of research on the effect of latency on social presence. To fill the gap, this paper aims to examine the impact of latency on social presence of VR conference and contrast it with that of traditional video conference. Here, the social presence is measured using the Networked Minds Social Presence Inventory (NMSPI). We design and conduct two conversation-based subjective tests for both types of conference and compare the impact of the latency based on the test results. The conclusions of these studies can be used as guidelines for VR service providers to optimize their conference systems.

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Are Standard CNNs Good Enough for No-Reference Stereoscopic image Quality Assessment?

Are Standard CNNs Good Enough for No-Reference Stereoscopic ...

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International conference on Signal processing and communications, SPCOM

作者： Ishita Bardhan Sumohana Channappayya Abhik Banerjee Abdur Rahim Mohammad Forkan Prem Jayaraman Abhinav Kumar Dept. of Artificial Intelligence Indian Institute of Technology Hyderabad India Dept. of Computing Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

Perceptual quality metrics derived from deep features have led to a boost in modelling the Human visual System (HVS) to perceive the quality of visual content. In this work, we study the effectiveness of fine-tuning three standard convolutional neural networks (CNNs) viz. ResNet50, VGG16 and MobileNetV2 to predict the quality of stereoscopic images in the no-reference setting. This work also aims to understand the impact of using disparity maps for quality prediction. Interestingly, our experiments demonstrate that disparity maps do not significantly contribute to improving perceptual quality estimation in the deep learning framework. To the best of our knowledge, this is the first study that explores the impact of disparity along with the chosen models for Stereoscopic image Quality Assessment. We present a detailed study of our experiments with various architectural configurations on the LIVE Phase I and II datasets. Further, our results demonstrate the innate capability of deep features for quality prediction. Finally, the simple fine-tuning of the models results in solutions that compete with state-of-the-art patch-based stereoscopic image quality assessment methods.

关键词： image quality Measurement Deep learning visualization Stereo image processing Estimation visual systems

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Packed Regions Information SEI Message

Packed Regions Information SEI Message

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IEEE visual communications and image processing (VCIP)

作者： Jill Boyce Miska M. Hannuksela Honglei Zhang Antti Hallapuro Nokia

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Specific regions of interest (ROIs) within a video are of greater interest than the remainder of the video for many use cases. The Packed Regions Information (PRI) Supplemental Enhancement Information (SEI) message enables packing of rectangular ROIs from an original picture into a smaller resolution picture for video coding, reducing pixel rate and bitrate. The SEI message signals metadata describing the size and position of the ROIs in the coded picture and in the original picture. Decoders may use the metadata to reconstruct target pictures at the original resolution from the decoded pictures containing the packed regions. The PRI SEI message is under consideration for potential inclusion in a future version of the Versatile Supplemental Enhanced Information (VSEI) standard. Experimental results are provided for use of the PRI SEI message with test conditions for machine analysis of coded video content, showing reductions in bitrate and pixel rate.

关键词： Video coding image resolution image coding visual communication Bit rate Metadata Decoding Standards Signal resolution image reconstruction

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Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification

Seq-Masks: Bridging the gap between appearance and gait mode...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Chang, Zhigang Yang, Zhao Chen, Yongbiao Zhou, Qin Zheng, Shibao Shanghai Jiao Tong Univ Inst Image Proc & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9781728185514

Video-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods focus on exploring appearance information, thus, vulnerable against illumination changes, scene noises, camera parameters, and especially clothes/carrying variations. Gait recognition provides an implicit biometric solution to alleviate the above headache. Nonetheless, it experiences severe performance degeneration as camera view varies. In an attempt to address these problems, in this paper, we propose a framework that utilizes the sequence masks (SeqMasks) in the video to integrate appearance information and gait modeling in a close fashion. Specifically, to sufficiently validate the effectiveness of our method, we build a novel dataset named MaskMARS based on MARS. Comprehensive experiments on our proposed large wild video Re-ID dataset MaskMARS evidenced our extraordinary performance and generalization capability. Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model. Our codes and dataset MaskMARS will be open-sourced as a strong baseline.

关键词： Multi-modal Fusion Video-based Person Re-ID Gait Recognition appearance model

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Quantizing Neural Networks with Knowledge Distillation for Efficient Video Quality Assessment

Quantizing Neural Networks with Knowledge Distillation for E...

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IEEE visual communications and image processing (VCIP)

作者： Jiayuan Yu Yingming Li College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

No-Reference Video Quality Assessment (NR-VQA) refers to evaluating the perceptual quality of videos without access to the original reference videos. However, existing deep NR-VQA methods often entail significant computational overhead, posing challenges in deploying these models in practical applications. In this paper, we focus on developing lightweight no-reference video quality assessment models and propose an Enhanced Quantization with Knowledge Distillation (EQKD) strategy to perform model compression while maintaining competitive performance. Specifically, we first introduce quantization-aware training in the VQA model parameter learning, which significantly reduces the memory footprint of the VQA models. Additionally, we incorporate knowledge distillation into the training process to further enhance the performance. The experimental results on three public datasets demonstrate the superiority of the proposed models over the state-of-the-art.

关键词： Training Knowledge engineering Quantization (signal) visual communication Computational modeling image processing Neural networks Quality assessment Video recording

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Comparitive Analysis of Various Transfer Learning Apporaches in Deep CNNs for image Classification 6th

Comparitive Analysis of Various Transfer Learning Apporaches...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Tyagi, Arnav Khandelwal, Rishabh Shelke, Nitin Arvind Singh, Jagendra Rajpal, Dev Gaware, Ishaan Rajendra School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031530814

Deep learning has become an effective approach over the past few years to addressing intricate computer vision problems, and Convolutional Neural Networks (CNNs) have been the primary driving force behind this progress. Developing CNNs, however, comes with the obstacle of requiring huge, labeled datasets. Gathering and annotating a large dataset for any specific job is costly and time-consuming. To overcome this challenge, researchers can employ transfer learning, a technique that involves using pre-trained deep learning models on extensive datasets. This study primarily aims to investigate the application of various transfer learning methods in conjunction with Deep Convolutional Neural Networks (CNNs) for image classification. The research utilizes the visual Object Classes Challenge 2012 (VOC2012) dataset as the foundation for its analysis. To classify a diverse range of object images, the study applies well-established transfer learning techniques, specifically fine-tuning pre-trained CNN models. Model performance is assessed through metrics like FPS (frames per second) and mAP% (mean average precision). A variety of models, including VGG19, SqueezeNet, YOLOv7, and Inception-ResNet-v2, are tested to determine the most suitable model for the dataset. The study’s findings demonstrate that employing transfer learning with CNNs can substantially enhance image classification accuracy on the dataset while reducing model development time. Notably, the Inception-ResNet-v2 model emerged as the top-performer, achieving an mAP score of 77%. This research underscores the potential of transfer learning as a powerful tool in the realm of image classification. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural image Compression

Semantic Ensemble Loss and Latent Refinement for High-Fideli...

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IEEE visual communications and image processing (VCIP)

作者： Daxin Li Yuanchao Bai Kai Wang Junjun Jiang Xianming Liu Faculty of Computing Harbin Institute of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually displeasing artifacts, such as blurring, color shifting, and texture loss, thereby compromising perceptual quality of images. To address these issues, this study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss, to enhance the perceptual quality of image reconstructions. Additionally, we have implemented a latent refinement process to generate content-aware latent codes. These codes adhere to bit-rate constraints, and prioritize bit allocation to regions of greater importance. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.

关键词： visualization image coding Codecs Codes Quantization (signal) visual communication image color analysis Semantics Bit rate image reconstruction

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image Enhancement Techniques Applied to Magnetic Resonance images: Multiple sclerosis 6

Image Enhancement Techniques Applied to Magnetic Resonance I...

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6th International conference on Advanced Technologies for Signal and image processing, ATSIP 2022

作者： Mnassri, Besma Echtioui, Amira Kallel, Fathi Dammak, Mariem Mhiri, Chokri Hamida, Ahmed Ben Atms Lab Advanced Technologies for Medicine and Signals Enis Sfax University Sfax Tunisia National School of Electronics and Communications Sfax University Sfax Tunisia Neurology Department Chu Habib Bourguiba Sfax Tunisia

ISBN: (纸本)9781665451161

As the most crucial step in image preprocessing techniques, Contrast enhancement (CE) is utilized to improve low contrast images quality with the aim to ameliorate the image's visual appearance for later automated processing (detection, segmentation and recognition). Due to this importance, several techniques were proposed to treat this topic. The present manuscript proposes comparative research across some contrast enhancement methods mainly: Global Histogram Equalization (GHE), Adaptive Gamma Correction (AGC) and knee function and gamma correction based on Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) method (KGC-DWT-SVD). These methods had been applied on brain and spinal cord MR (Magnetic Resonance) images of patients affected with multiple sclerosis. The obtained results had been evaluated according to the following measurement metrics: the entropy (H), peak signal to noise ratio (PSNR), mean squared error (MSE), structure similarity index measurement (SSIM) and feature similarity index measurement (FSIM). © 2022 IEEE.

关键词： Singular value decomposition

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2022 IEEE 14th International conference on Wireless communications and Signal processing, WCSP 2022

2022 IEEE 14th International Conference on Wireless Communic...

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14th IEEE International conference on Wireless communications and Signal processing, WCSP 2022

ISBN: (纸本)9781665450850

The proceedings contain 216 papers. The topics discussed include: block propagation in blockchain-based 6g network: from a communication perspective;location-based reliable sharding in blockchain-enabled fog computing networks;application-based cooperative content caching via tensor domain adaptation networks;power coverage optimization in cellular radio networks using neural architecture search;blockchain-enabled edge computing framework for hierarchic cluster-based federated learning;joint mining offloading and computing resource optimization in mobile blockchain system;low-cost traffic perception for road detector data estimation: a deep learning approach;a lightweight network with lane feature enhancement for multilane drivable area detection;large kernel attention hashing for efficient image retrieval;machine learning-based multi-classification for first-episode schizophrenics, ultra-high risk schizophrenics, and healthy controls;underwater image restoration based on an improved underwater image formation model;efficient shared feature learning for cross-modality person re-identification;and building scale-attentive visual transformers for intelligent electric power system inspection.

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