检索结果-内蒙古大学图书馆

Fast encoding techniques for multiview video coding

SIGNAL PROCESSING-IMAGE COMMUNICATION 2013年第6期28卷 569-580页

作者： Khattak, S. Hamzaoui, R. Ahmad, S. Frossard, P. De Montfort Univ Ctr Elect & Commun Engn Leicester LE1 9BH Leics England Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland

multiview video coding (MVC) is a technique that permits efficient compression of multiview video. MVC uses variable block size motion and disparity estimation for block matching. This requires an exhaustive search process that involves all possible macro-block partition sizes. We analyze the time complexity of MVC and the methods that have been proposed to speed up motion and disparity estimation. We then propose two new methods: Previous Disparity Vector Disparity Estimation (PDV-DE) and Stereo-Motion Consistency Constraint Motion and Disparity Estimation (SMCC-MDE). PDV-DE exploits the correlation between temporal levels and disparity vectors to speed up the disparity estimation process while SMCC-MDE exploits the geometrical relationship of consecutive frame pairs to speed up motion and disparity estimation. We build a complete low complexity MVC encoding solution that combines our two methods with complementary previous methods to speed up motion and disparity search. We evaluate the complexity of our solution in terms of encoding time and number of search points. Our experimental results show that our solution can reduce the encoding time and number of search points of the standard MVC implementation (JMVM 6.0) using the fast TZ search mode up to 93.7% and 96.9%, respectively, with negligible degradation in the rate-distortion performance. Compared to the best published results, this is an improvement of up to 11% and 7%, respectively. (C) 2013 Elsevier B.V. All rights reserved.

关键词： multiview video coding Disparity estimation Motion estimation

来源：评论

学校读者我要写书评

暂无评论

Fast synthesized and predicted just noticeable distortion maps for perceptual multiview video coding

引用

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2013年第6期24卷 700-707页

作者： Gao, Yu Xiu, Xiaoyu Liang, Jie Lin, Weisi Simon Fraser Univ Burnaby BC V5A 1S6 Canada Nanyang Technol Univ Singapore 639798 Singapore

The just noticeable distortion (JND) map is a useful tool for perceptual video coding. However, direct calculation of the JND map incurs high complexity, and the problem is aggravated in multiview video coding. In this paper, two fast methods are proposed to generate the JND maps of multiview videos. In the first method, the JND maps of some anchor views are used to synthesize the JND maps of other views via the depth image based rendering (DIBR), which can be much faster than direct JND computation. In the second method, the motion and disparity vectors obtained during the video coding are employed to predict the JND maps. If the prediction is not satisfactory, the JND block will be refreshed by calculating the JND directly. This method does not need any camera parameters and depth maps. The performances of the two fast JND map generation methods are evaluated in a perceptual MVC framework, where the residuals after spatial, temporal, or inter-view prediction are tuned according to the JND thresholds to save the bits without affecting the perceptual quality. Experimental results show that the JND prediction method has better accuracy and lower complexity. In addition, both fast JND methods lead to negligible degradation of the coding performance, compared to the direct JND method. (C) 2012 Elsevier Inc. All rights reserved.

关键词： multiview video coding Just noticeable distortion Depth image based rendering Motion estimation and compensation Computational complexity Perceptual video coding Human visual system Block based prediction

来源：评论

学校读者我要写书评

暂无评论

A novel fast search method to find disparity vectors in multiview video coding

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2021年第7期80卷 10821-10837页

作者： Zandi, Ghane Roodaki, Hoda Shirmohammadi, Shervin KN Toosi Univ Technol Tehran Iran Univ Ottawa Distributed & Collaborat Virtual Environm Res Lab Ottawa ON Canada

In multiview/3D video, the amount of data to be transmitted to the decoder increases proportionally with the number of cameras. One way to efficiently compress such video is to use multiview video coding (MVC) which simultaneously reduces temporal and spatial redundancy within the same view and among multiple views. But, existing disparity estimation methods used to extract the redundancy among views are typically too computationally complex. In this paper, we propose a method for fast disparity estimation in multiview/3D video. First, for each block of the predicted frame, the view-dependent geometry and the depth information are used to find the corresponding block in the reference views. Then, a fast and greedy search method is proposed to search the surrounding areas to find the most similar block. Simulation results show that our proposed method achieves better bitrate and quality with much lower computational complexity compared to state-of-the-art methods.

关键词： multiview disparity estimation View-dependent geometry Depth information multiview video coding

来源：评论

学校读者我要写书评

暂无评论

Efficient Multi-Reference Frame Selection Algorithm for Hierarchical B Pictures in multiview video coding

引用

IEEE TRANSACTIONS ON BROADCASTING 2011年第1期57卷 15-23页

作者： Zhang, Yun Kwong, Sam Jiang, Gangyi Wang, Hanli Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Ningbo Univ Fac Informat Sci & Engn Ningbo 315000 Zhejiang Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 201804 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Multiple reference frame prediction technology is adopted in multiview video coding (MVC) to explore temporal and inter-view redundancies of multiview videos, resulting in extremely high encoding complexity by searching the best reference frame indices and the best reference directions for each macroblock (MB). In order to reduce MVC coding computations while keeping the coding efficiency and thus to advance MVC in real-time multimedia broadcasting applications, we propose a Fast Multi-reference Frame Selection Algorithm (FMFSA) for hierarchical B picture prediction structure in this paper. Due to high spatial correlations within a MB, there is a high probability for smaller MB partition modes selecting the same reference frame and direction as B16 x 16 does. Therefore, the reference information of latter checked MB partition modes can be directly set according to the reference information of previous examined mode. Experimental results on MVC show that the proposed FMFSA can achieve 68.34% similar to 79.01% total encoding time reduction while the average bit rate increase and peak signal-to-noise ratio degradation are within 0.54% and 0.04 dB, respectively for test multiview sequences with various motion properties and camera arrangements.

关键词： Hierarchical B picture multi-reference frame prediction multiview video coding

来源：评论

学校读者我要写书评

暂无评论

SWGNet: Step-Wise Reference Frame Generation Network for multiview video coding

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR video TECHNOLOGY 2024年第4期34卷 2949-2958页

作者： Zhang, Jing Hou, Yonghong Pan, Zhaoqing Peng, Bo Ling, Nam Lei, Jianjun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

In multiview video coding, the coding performance highly depends on the quality of the reference frames. In view of this, a step-wise reference frame generation network (SWGNet) is designed to improve the quality of the reference frame for efficient multiview video coding. In particular, a frame-level to block-level learning paradigm is proposed to step-wisely generate a high-quality reference frame. In the frame-level stage, by exploiting parallax correlations between temporal and inter-view references on the basis of image alignment, a parallax-guided frame-level synthesis module is proposed to generate an elementary reference frame. Then, in the block-level stage, a transformer-based block-level aggregation module is designed to further refine the texture details of the reference frame by modeling long-range dependencies among pixels. The proposed SWGNet is integrated into 3D-HEVC, and extensive experiments demonstrate that the proposed method achieves significant bitrate saving compared with 3D-HEVC.

关键词： video coding Encoding Correlation Transformers Redundancy Context modeling Bit rate Reference frame generation inter-view correlation multiview video coding convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

A low energy adaptive motion estimation hardware for H.264 multiview video coding

引用

JOURNAL OF REAL-TIME IMAGE PROCESSING 2018年第1期15卷 3-12页

作者： Aksehir, Yusuf Erdayandi, Kamil Ozcan, Tevfik Zafer Hamzaoglu, Ilker Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey

multiview video coding (MVC) is the process of efficiently compressing stereo (two views) or multiview video signals. The improved compression efficiency achieved by H.264 MVC comes with a significant increase in computational complexity. Temporal prediction and inter-view prediction are the most computationally intensive parts of H.264 MVC. Therefore, in this paper, we propose novel techniques for reducing the amount of computations performed by temporal and inter-view predictions in H.264 MVC. The proposed techniques reduce the amount of computations performed by temporal and inter-view predictions significantly with very small PSNR loss and bit rate increase. We also propose a low energy adaptive H.264 MVC motion estimation hardware for implementing the temporal and inter-view predictions including the proposed computation reduction techniques. The proposed hardware is implemented in Verilog HDL and mapped to a Xilinx Virtex-6 FPGA. The FPGA implementation is capable of processing 30 x 8 = 240 frames per second (fps) of CIF (352 x 288) size eight view video sequence or 30 x 2 = 60 fps of VGA (640 x 480) size stereo (two views) video sequence. The proposed techniques reduce the energy consumption of this hardware significantly.

关键词： H.264 multiview video coding Motion estimation Hardware implementation FPGA

来源：评论

学校读者我要写书评

暂无评论

Content-Aware Prediction Algorithm With Inter-View Mode Decision for multiview video coding

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2008年第8期10卷 1553-1564页

作者： Ding, Li-Fu Tsung, Pei-Kuei Chien, Shao-Yi Chen, Wei-Yin Chen, Liang-Gee Natl Taiwan Univ Grad Inst Elect Engn DSP IC Design Lab Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Dept Elect Engn Media IC & Syst Lab Taipei 10617 Taiwan

3-D video will become one of the most significant video technologies in the next-generation television. Due to the ultra high data bandwidth requirement for 3-D video, effective compression technology becomes an essential part in the infrastructure. Thus multiview video coding (MVC) plays a critical role. However, MVC systems require much more memory bandwidth and computational complexity relative to mono-view video coding systems. Therefore, an efficient prediction scheme is necessary for encoding. In this paper, a new fast prediction algorithm, content-aware prediction algorithm (CAPA) with inter-view mode decision, is proposed. By utilizing disparity estimation (DE) to find corresponding blocks between different views, the coding information, such as rate-distortion cost, coding modes, and motion vectors, can be effectively shared and reused from the coded view channel. Therefore, the computation for motion estimation (ME) in most view channels can be greatly reduced. Experimental results show that compared with the full search block matching algorithm (FSBMA) applied to both ME and DE, the proposed algorithm saves 98.4-99.1% computational complexity of ME in most view channels with negligible quality loss of only 0.03-0.06 dB in PSNR.

关键词： 3-D video disparity estimation H264/AVC motion estimation multiview video coding

来源：评论

学校读者我要写书评

暂无评论

An Empirical Model of multiview video coding Efficiency for Wireless Multimedia Sensor Networks

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2013年第8期15卷 1800-1814页

作者： Colonnese, Stefania Cuomo, Francesca Melodia, Tommaso Univ Roma La Sapienza DIET I-00184 Rome Italy SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

We develop an empirical model of the multiview video coding (MVC) performance that can be used to identify and separate situations when MVC is beneficial from cases when its use is detrimental in wireless multimedia sensor networks (WMSN). The model predicts the compression performance of MVC as a function of the correlation between cameras with overlapping fields of view. We define the common sensed area (CSA) between different views, and emphasize that it depends not only on geometrical relationships among the relative positions of different cameras, but also on various object-related phenomena, e. g., occlusions and motion, and on low-level phenomena such as variations in illumination. With these premises, we first experimentally characterize the relationship between MVC compression gain (with respect to single view video coding) and the CSA between views. Our experiments are based on the H. 264 MVC standard, and on a low-complexity estimator of the CSA that can be computed with low inter-node signaling overhead. Then, we propose a compact empirical model of the efficiency of MVC as a function of the CSA between views, and we validate the model with different multiview video sequences. Finally, we show how the model can be applied to typical scenarios in WMSN, i.e., to clustered or multi-hop topologies, and we show a few promising results of its application in the definition of cross-layer clustering and data aggregation procedures.

关键词： multiview video coding MVC efficiency model video sensor networks.

来源：评论

学校读者我要写书评

暂无评论

Disparity-Aware Reference Frame Generation Network for multiview video coding

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 4515-4526页

作者： Lei, Jianjun Zhang, Zongqian Pan, Zhaoqing Liu, Dong Liu, Xiangrui Chen, Ying Ling, Nam Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Alibaba Grp Hangzhou 310052 Peoples R China Univ Sci & Technol China CAS Key Lab Technol Geo Spatial Informat Proc & A Hefei 230027 Peoples R China Alibaba Grp Hangzhou 310052 Peoples R China Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

multiview video coding (MVC) aims to compress the multiview video through the elimination of video redundancies, where the quality of the reference frame directly affects the compression efficiency. In this paper, we propose a deep virtual reference frame generation method based on a disparity-aware reference frame generation network (DAG-Net) to transform the disparity relationship between different viewpoints and generate a more reliable reference frame. The proposed DAG-Net consists of a multi-level receptive field module, a disparity-aware alignment module, and a fusion reconstruction module. First, a multi-level receptive field module is designed to enlarge the receptive field, and extract the multi-scale deep features of the temporal and inter-view reference frames. Then, a disparity-aware alignment module is proposed to learn the disparity relationship, and perform disparity shift on the inter-view reference frame to align it with the temporal reference frame. Finally, a fusion reconstruction module is utilized to fuse the complementary information and generate a more reliable virtual reference frame. Experiments demonstrate that the proposed reference frame generation method achieves superior performance for multiview video coding.

关键词： Image coding video coding Deep learning Image reconstruction Estimation Encoding Task analysis multiview video coding reference frame generation disparity-aware alignment DAG-Net 3D-HEVC

来源：评论

学校读者我要写书评

暂无评论

Deep In-Loop Filtering via Multi-Domain Correlation Learning and Partition Constraint for multiview video coding

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR video TECHNOLOGY 2023年第4期33卷 1911-1921页

作者： Peng, Bo Chang, Renjie Pan, Zhaoqing Li, Ge Ling, Nam Lei, Jianjun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

The deep learning-based in-loop filtering methods have greatly improved the coding efficiency for High Efficiency video coding (HEVC). However, directly applying these HEVC-orientated in-loop filtering methods to multiview video coding may not obtain satisfactory performance due to the characteristics of multiview video. In this paper, a deep in-loop filtering method based on multi-domain correlation learning and partition constraint network (MDP-Net) is proposed to boost the multiview video coding performance. To the best of our knowledge, this work is the first attempt at deep in-loop filtering for multiview video coding. Specifically, a multi-domain correlation learning module is presented to restore the high-frequency details of the distorted frame by exploring the multi-domain correlations. Besides, based on the block partition information generated in video coding, a partition-constrained reconstruction module is proposed to better attenuate the compression artifacts by designing a partition loss. Finally, the proposed MDP-Net is integrated into 3D-HEVC reference software, and the experimental results demonstrate that the proposed method achieves considerable performance improvement compared with 3D-HEVC.

关键词： video coding Encoding Correlation Feature extraction Nonlinear distortion High efficiency video coding Convolutional neural networks multiview video coding multi-domain correlation learning partition constraint 3D-HEVC

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：