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

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Early Determination for Intra Block Copy Prediction with Refined Screen Content Detection

Early Determination for Intra Block Copy Prediction with Ref...

引用

IEEE visual communications and image processing (VCIP)

作者： Zhenhao Sun Meng Wang Yingwen Zhang Shiqi Wang Sam Kwong Department of Computer Science City University of Hong Kong China School of Data Science Lingnan University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The intra block copy (IBC) mode introduced can significantly improve the coding efficiency for screen content. However, with the mixed content, the coding performance improvement of IBC is limited and while also introducing the increases of the coding complexity. This paper proposes an early determination scheme for IBC prediction to reduce the encoding complexity over mixed content sequences containing captured content regions. Our method is based on a refined CTU-level screen content detection method, which can better distinguish screen content in mixed scenes. Experimental results show that the proposed method reduces encoding complexity with moderate coding performance loss on mixed content sequences.

关键词： Video coding image coding visual communication Semantics Text detection Complexity theory

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Real-Time Video-based Point Cloud Compression

Real-Time Video-based Point Cloud Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Louis Fréneau Guillaume Gautier Heikki Tampio Alexandre Mercat Jarno Vanne Ultra Video Group Tampere University Tampere Finland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper presents a demonstration setup for our open-source intra encoder called uvgVPCCenc, which is optimized for real-time Video-based Point Cloud Compression (V-PCC). uvgVPCCenc achieves an average encoding speed of 26 frames per second (fps) on an Intel i7-12700 CPU when encoding volumetric video sequences with up to 185 000 points per frame. It is shown to be 700 times as fast as TMC2 reference implementation for V-PCC. Our work is the first to demonstrate real-time intra V-PCC encoding on a consumer-grade desktop computer. It indicates that even the immense computational complexity of intra V-PCC encoding can be tackled for practical applications with effective design and optimization techniques.

关键词： Point cloud compression image coding visual communication Video sequences Streaming media Real-time systems Computational complexity Optimization

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An intricate spectrogram processing in speech enhancement using machine learning with cloud computing 7

An intricate spectrogram processing in speech enhancement us...

引用

7th International conference on image Information processing, ICIIP 2023

作者： Reddy, Yenna Geetha Mahendran, G. Al-Khaykan, Ameer Alkawak, Omar A. Kalaiselvan, S.A. Priya, A. Department of Computer Science And Engineering Kompally Telangana Secunderabad India R.M.K Engineering College Department of Mechancal Engineering Chennai Tamil Nadu Kavaraipettai India University of Kufa Department of Electronic And Communications Engineering Najaf Iraq Al-Mustaqbal University College Department of Air Conditioning And Refrigeration Technical Engineering Babil Hilla Iraq Department of Computer Science And Engineering Tamil Nadu Chennai India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9798350371406

The spectrogram is defined as the visual representation of the signal strength with a particular interval of time regarding frequencies in the appropriate waveform. It is demonstrated using the Fourier transform. It is illustrated in a three-dimensional plot of signal composed with amplitude versus frequency and time. They are classified as long signal sections and short signal sections with filters. It demonstrates the consecutive spectacle of the sound signal at a particular interval of time. The time-frequency representation of audio signals is obtained through spectrograms. This helps in the understanding of the speech data. Machine learning plays an important role in the identification of the relevant features in the spectrogram. The intricate patterns in the time and frequency domains are represented through CNN and RNN. These models are trained and tested through a large dataset which includes both clean and noisy speech samples. This helps to learn complex interrelationships and patterns associated with varied environmental conditions and interference systems. The computational demands of these systems are handled through the cloud computing techniques. They provide a platform for effective solutions for performing real-time interference. These cloud services provide various collaborations to numerous models to achieve desired outcomes. The proposed system provides higher adaptability in varied environmental and noisy profiles. Thus the integration of cloud computing with machine learning provides real-time processing with a natural seamless user experience. © 2023 IEEE.

关键词： Cloud computing

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A High-Efficiency and Low-Complexity SKIP Type for Base Mesh Coding in V-DMC

A High-Efficiency and Low-Complexity SKIP Type for Base Mesh...

引用

IEEE visual communications and image processing (VCIP)

作者： Jianfeng Xu Haruhisa Kato Kei Kawamura Advanced Visual Communication Laboratory KDDI Research Inc. Fujimino-shi Saitama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Video-based Dynamic Mesh Coding (V-DMC) is an emerging standard for dynamic mesh compression, where the original meshes are decimated into simplified meshes called base meshes. This paper introduces a novel SKIP type for base mesh coding in V-DMC, complementing the existing INTRA and INTER types. When the SKIP type is used in base mesh coding, it directly copies the reconstructed base mesh from the reference frame, eliminating the need for additional data coding. Thus, the reconstructed base mesh in the current frame is identical to that in the reference frame. This significantly reduces the bit rate and decoding time for base meshes. Additionally, this paper employs a Lagrangian cost function using a linear model for bit estimation of INTRA type and L1 norms for distortion approximation of SKIP type to enable the encoder to select the best type for base meshes. Experimental results demonstrate superior BD-rate performance and significantly reduced decoding time for base meshes using the SKIP type, particularly in sequences with minimal object movements.

关键词： image coding visual communication Bit rate Estimation Cost function Distortion Decoding image reconstruction Standards

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Binocular Rivalry and Fusion Mechanisms Based No-Reference Stereoscopic image Quality Assessment Considering Feedback Guidance

Binocular Rivalry and Fusion Mechanisms Based No-Reference S...

引用

IEEE visual communications and image processing (VCIP)

作者： Haoxiang Chang Sumei Li Dongyue He Chen Zhang Tianjin University Tianjin China

With the growing popularity of 3D content and virtual reality applications, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods have become increasingly important. In this paper, we propose a convolutional neural network (CNN) SIQA model based on binocular rivalry and fusion mechanisms of the human visual system (HVS). In order to get a better representation of binocular information, we propose Two-Stage Enhanced Fusion Module (TSEFM) that consists of two stages for monocular features enhancement and binocular features fusion, respectively. Given the dynamical characteristics of binocular rivalry phenomenon, the proposed Content-Aware Binocular Rivalry Fusion Module (CABRFM) dynamically and adaptively adjusts its output based on the input content. Additionally, considering that feedback mechanism of HVS is indispensable and significant, we introduce feedback connections during feature aggregation to realize the guidance of high-level features to low-level features. Extensive experimental results demonstrate the superiority of our method over state-of-the-art metrics, showcasing its excellent performance.

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An algorithm for parallel processing of traffic signs video on a graphics processor

An algorithm for parallel processing of traffic signs video ...

引用

2022 International conference on Information Science and communications Technologies, ICISCT 2022

作者： Umarov, Mukhriddin Elov, Jamshid Khalilov, Sirojiddin Narzullayev, Inomjon Karimov, Marat Samarkand Br. of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Software Engineering Samarkand Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Nurafshan Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Information Technologies Tashkent Uzbekistan

ISBN: (纸本)9781665472296

In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects. © 2022 IEEE.

关键词： image enhancement

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Dual-Teacher Color and Structure Distillation for NIR image Colorization

Dual-Teacher Color and Structure Distillation for NIR Image ...

引用

IEEE visual communications and image processing (VCIP)

作者： Tae-Sung Park Jong-Ok Kim School of Electrical Engineering Korea University Seoul Korea

Near-infrared (NIR) imaging can acquire more details and textures with less noise in low-light environments compared to RGB. As a result, it has been widely used in low-light vision scenarios such as CCTV, autonomous driving, and military applications. However, applying NIR images to the human cognitive system and computer vision algorithms is challenging due to the lack of color information. Therefore, it is essential to colorize NIR images into RGB ones. We propose a teacher-student deep network with dual-teacher knowledge distillation to better estimate the original color and structure information. Specifically, our dual-teacher network is designed to separately teach distinct knowledge, such as color and structure qualities in an image, to the student. Finally, a color guided structure (CGS) and color embedding (CE) module are applied to fuse color and structure features. Our trained model can retain color consistency and detailed structure information of objects.

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A Fast Monocular visual-Inertial Odometry Using Point and Line Features 7

A Fast Monocular Visual-Inertial Odometry Using Point and Li...

引用

7th International conference on Signal and image processing, ICSIP 2022

作者： Shen, Weixiang Jia, Yongxing Zhu, Junchao Qian, Xiaojian Army Engineering University of PLA College of Communications Engineering Nanjing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Most SLAM systems using point feature reduce the pose accuracy or crash due to the small number of point features are extracted under the light-dark mutation environment or low texture area. In order to improve the robustness and pose accuracy of the SLAM, a fast monocular visual-inertial odometry using point and line features is proposed in this paper. Firstly, the point and line features of the image are extracted for feature matching, and the IMU measurement data is extracted as the initialization state estimation information of the sliding window. The following IMU measurements are pre-integrated to constrain the successive IMU body states during optimization. Secondly, the proposed VIO uses geometric constraints to delete point-line matching outliers, and point-line feature endpoints are projected into a normalized plane for triangulation to build re-projection residuals. Finally, after obtaining the initial estimation of this system and the IMU body state predicted by IMU measurement, the sliding window optimization method is used to get the precise position of camera. The experiments are evaluated on public datasets (EuRoc MAV dataset and PennCOSYVIO dataset). The pose accuracy of this algorithm is more accurate than VINS-mono, and the speed is faster than PL-VIO. © 2022 IEEE.

关键词： Textures

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Draft-and-Revise: Effective image Generation with Contextual RQ-Transformer 36

Draft-and-Revise: Effective Image Generation with Contextual...

引用

36th conference on Neural Information processing Systems (NeurIPS)

作者： Lee, Doyup Kim, Chiheon Kim, Saehoon Cho, Minsu Han, Wook-Shin Kakao Brain Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9781713871088

Although autoregressive models have achieved promising results on image generation, their unidirectional generation process prevents the resultant images from fully reflecting global contexts. To address the issue, we propose an effective image generation framework of Draft-and-Revise with Contextual RQ-transformer to consider global contexts during the generation process. As a generalized VQ-VAE, RQ-VAE first represents a high-resolution image as a sequence of discrete code stacks. After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image. Then, we propose the two-phase decoding, Draft-and-Revise, for Contextual RQ-Transformer to generate an image, while fully exploiting the global contexts of the image during the generation process. Specifically. in the draft phase, our model first focuses on generating diverse images despite rather low quality. Then, in the revise phase, the model iteratively improves the quality of images, while preserving the global contexts of generated images. In experiments, our method achieves state-of-the-art results on conditional image generation. We also validate that the Draft-and-Revise decoding can achieve high performance by effectively controlling the quality-diversity trade-off in image generation.

关键词： Decoding

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Memory-Efficient Point Cloud Registration via Overlapping Region Sampling

Memory-Efficient Point Cloud Registration via Overlapping Re...

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IEEE visual communications and image processing (VCIP)

作者： Tomoyasu Shimada Kazuhiko Murasaki Shogo Sato Toshihiko Nishimura Taiga Yoshida Ryuichi Tanida NTT Corporation Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advances in deep learning have improved 3D point cloud registration but increased graphics processing unit (GPU) memory usage, often requiring preliminary sampling that reduces accuracy. We propose an overlapping region sampling method to reduce memory usage while maintaining accuracy. Our approach estimates the overlapping region and intensively samples from it, using a k-nearest-neighbor (kNN) based point compression mechanism with multi layer perceptron (MLP) and transformer architectures. Evaluations on 3DMatch and 3DLoMatch datasets show our method outperforms other sampling methods in registration recall, especially at lower GPU memory levels. For 3DMatch, we achieve 94% recall with 33% reduced memory usage, with greater advantages in 3DLoMatch. Our method enables efficient large-scale point cloud registration in resource-constrained environments, maintaining high accuracy while significantly reducing memory requirements.

关键词： Point cloud compression Three-dimensional displays Accuracy image coding visual communication Memory management Graphics processing units Sampling methods Transformers Hardware

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